{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication\Log\Master.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}30 Aug 2024, 09:40:48

{com}. do "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication\Master.do"
{txt}
{com}. *****************************************************************************
. *               Master Do File                                              *
. *                                                                                                                                           *
. * Author:                       Valentina Gonzalez-Rostani                                                      *
. * Contact:                      mag384@pitt.edu                                                                 *
. * Date:                         August 26 2024                                                                          *
. * Version:                      Stata 17                                                                        *
. *                                                                                                                                                       *
. *****************************************************************************
. /*
> This do-file is a master that runs all do-files in the folder `do`
> 
> */
. 
. ** Set directory
. cd "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication"
{res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication
{txt}
{com}. 
. do "./Do/1_1_Switching_US.do"
{txt}
{com}. *****************************************************************************
. *               Cleaning and Analyzing - Switching vote US                  *
. *                                                                                                                                           *
. * Author:                       Valentina Gonzalez-Rostani                                                      *
. * Contact:                      mag384@pitt.edu                                                                 *
. * Date:                         March 21 2021                                                                   *
. * Version:                      Stata 17                                                                        *
. *                                                                                                                                                       *
. *****************************************************************************
. /*
> This do-file:
>         * Processing of the data
>                 * Call the Data
>                 * Define variables
>                 * Save the data
>         * Load preapred data **line 794**
>         * Analysis and Descriptives: Export Tables &  Figure
> 
> Input: GSS data
>         - Data\Switching\GSS7218_R3.dta
> 
> Final output:
>         Cleaned data: 
>                 * "Data\GSS.dta" this data contains the relevant variables for the analysis.
>         Tables:
>                 * table A3: Switching Vote, IV - RTI, US [Table\USlong_2.tex]
>                 * table A5: Switching Vote (alternative definition), IV - RTI [Table\USStrict_2.tex]
>                 * table A6: Switching Vote, IV - Routine (dummy), US [Table\USdummy_2.tex]
>                 * table A1: Descriptive statistic: USA GSS 2016 vs 2012
>         Figure:
>             * Figure 3: The effect of exposure to automation on vote-switching. [US Part]  [Figure\US.gph]
> 
> 
> */
. 
. 
. ** Set directory
. cd "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication"
{res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication
{txt}
{com}. 
. *##########################################
. * Processing of the data (alternatively skip and go to line 794)
. *##########################################
. {c -(}
. *##########################################
. * Calling the data
. *##########################################
. {c -(}
. * Clearing and allowing multiple variables
. * Set the directory
. * If re-running clear mata and clear matrix
. clear
. set maxvar 30000

. 
. * Calling the data
. use "Data\Switching\GSS7218_R3.dta", clear
. {c )-}
. *############################################
. * Creating Variables
. *############################################
. {c -(}
. ************************************************************************
. ********************* B. Demographic  **********************************
. ************************************************************************
. {c -(}
. * Generate a dummy variable for unemployment status
. gen unemployed = (wrkstat==4)
. 
. * Generate a dummy variable for female
. gen female=(sex==2)
. 
. * Generate a dummy variable for black race
. gen black = (race==2)
. 
. * Dummy for born outside the US
. gen foreign = .  // Initialize the variable 'foreign' with missing values
{txt}(64,814 missing values generated)
{com}. replace foreign=1 if born==2
{txt}(5,110 real changes made)
{com}. replace foreign=0 if born==1
{txt}(50,441 real changes made)
{com}. 
. * Dummy for nonreligion
. gen nonrelig=.
{txt}(64,814 missing values generated)
{com}. replace nonrelig=1 if relig==4
{txt}(7,797 real changes made)
{com}. replace nonrelig=0 if relig~=4 & relig~=.
{txt}(57,017 real changes made)
{com}. 
. {c )-}
. ************************************************************************
. ********************* C. Standarizing occupations **********************
. ************************************************************************
. {c -(}
. */ In this section I will standarize occupations. I follow Thewissen and Rueda (2019)  code, which was based on Harry Ganzeboom's correspondence file, who used ILO correspondence table.
. 
. * using code from Harry Ganzeboom, I convert occupations from ISCO-08 to ISCO-88. 
. * I am doing this conversion because the RTI is in 08 ISCO code. 
. gen iscoco = isco88
{txt}(3,902 missing values generated)
{com}. 
. */ Given that Goos et al (2014) code is designed for 2-digit. I create a new variable that will transform iscoco into a 2 digit variables. 
. 
. foreach var of varlist iscoco isco08 {c -(}
{txt}  2{com}. gen str_`var'=string(`var')
{txt}  3{com}. replace str_`var'="0100" if str_`var'=="100"
{txt}  4{com}. {c )-}
{txt}(0 real changes made)
(0 real changes made)
{com}. sum iscoco if str_iscoco=="0100"

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}iscoco {c |}{res}          0
{com}. 
. 
. foreach x in iscoco {c -(}
{txt}  2{com}. gen `x'2 = substr(str_`x',1,2) if `x'<9334
{txt}  3{com}. replace `x'2 = "." if `x'2==""
{txt}  4{com}. {c )-}
{txt}(4,765 missing values generated)
(4,765 real changes made)
{com}. 
. drop str_*
. 
. destring iscoco2, replace
{txt}iscoco2: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(4765 missing values generated)
{res}{com}. 
. label var iscoco2 "ISCO88 at the 2-digit"
. 
. * Labels Iscoco
. {c -(}
. gen iscoco2n="."
. replace iscoco2n="Legislators and senior officials" if iscoco2==11
{txt}variable {bf}{res}iscoco2n{sf}{txt} was {bf}{res}str1{sf}{txt} now {bf}{res}str32{sf}
{txt}(469 real changes made)
{com}.         replace iscoco2n="Corporate managers" if iscoco2==12
{txt}(6,362 real changes made)
{com}.         replace iscoco2n="General managers" if iscoco2==13
{txt}(1,383 real changes made)
{com}.         replace iscoco2n="Physical, mathematical and engineering science professionals" if iscoco2==21
{txt}variable {bf}{res}iscoco2n{sf}{txt} was {bf}{res}str32{sf}{txt} now {bf}{res}str60{sf}
{txt}(2,027 real changes made)
{com}.         replace iscoco2n="Life science and health professionals" if iscoco2==22
{txt}(1,464 real changes made)
{com}.         replace iscoco2n="Teaching professionals" if iscoco2==23
{txt}(3,200 real changes made)
{com}.         replace iscoco2n="Other professionals" if iscoco2==24
{txt}(3,113 real changes made)
{com}.         replace iscoco2n="Physical and engineering science associate professionals" if iscoco2==31
{txt}(1,492 real changes made)
{com}.         replace iscoco2n="Life science and health associate professionals" if iscoco2==32
{txt}(2,939 real changes made)
{com}.         replace iscoco2n="Teaching associate professionals" if iscoco2==33
{txt}(365 real changes made)
{com}.         replace iscoco2n="Other associate professionals" if iscoco2==34
{txt}(3,500 real changes made)
{com}.         replace iscoco2n="Office clerks" if iscoco2==41
{txt}(7,305 real changes made)
{com}.         replace iscoco2n="Customer services clerks" if iscoco2==42
{txt}(2,000 real changes made)
{com}.         replace iscoco2n="Personal and protective services workers" if iscoco2==51
{txt}(5,200 real changes made)
{com}.         replace iscoco2n="Models, salespersons and demonstrators" if iscoco2==52
{txt}(1,737 real changes made)
{com}.         replace iscoco2n="Market-oriented skilled agricultural and fishery workers" if iscoco2==61
{txt}(490 real changes made)
{com}.         replace iscoco2n="Subsistence agricultural and fishery workers" if iscoco2==62
{txt}(0 real changes made)
{com}.         replace iscoco2n="Extraction and building trades workers" if iscoco2==71
{txt}(1,837 real changes made)
{com}.         replace iscoco2n="Metal, machinery and related trades workers" if iscoco2==72
{txt}(3,409 real changes made)
{com}.         replace iscoco2n="Precision, handicraft, printing and related trades workers" if iscoco2==73
{txt}(235 real changes made)
{com}.         replace iscoco2n="Other craft and related trades workers" if iscoco2==74
{txt}(602 real changes made)
{com}.         replace iscoco2n="Stationary-plant and related operators" if iscoco2==81
{txt}(505 real changes made)
{com}.         replace iscoco2n="Machine operators and assemblers" if iscoco2==82
{txt}(2,820 real changes made)
{com}.         replace iscoco2n="Drivers and mobile-plant operators" if iscoco2==83
{txt}(2,096 real changes made)
{com}.         replace iscoco2n="Sales and services elementary occupations" if iscoco2==91
{txt}(2,876 real changes made)
{com}.         replace iscoco2n="Agricultural, fishery and related labourers" if iscoco2==92
{txt}(435 real changes made)
{com}.         replace iscoco2n="Labourers in mining, construction, manufacturing and transport" if iscoco2==93
{txt}variable {bf}{res}iscoco2n{sf}{txt} was {bf}{res}str60{sf}{txt} now {bf}{res}str62{sf}
{txt}(1,639 real changes made)
{com}.         replace iscoco2n="Armed forces" if iscoco2==01
{txt}(0 real changes made)
{com}. label var iscoco2n "Names of iscoco2 coding"
. {c )-}
. 
. 
. 
. 
. 
. ************************************************************************
. ********************* Exposure to automation ***************************
. ************************************************************************
. {c -(}
. ************************************************************************
. * 1. Goos et al.(2014)
. ************************************************************************
. {c -(}
. ** These lines include the routinisation and offshoring indices at the 2-digit level from Goos et al. (2014)
. 
. */ The idea is that for each occupation (at the 2-digit level) one risk is assigned. The weak part of this index is that it treats as the same the last 2-digit of the ISCO.
. * In other words, ISCO has 4 digit, but goos et al only consider the first 2 digit, therefore there is no differentiation among occupations that differ in the last 2 digit. 
. gen rti=.
{txt}(64,814 missing values generated)
{com}. 
. {c -(}
. replace rti=-0.75 if iscoco2==12
{txt}(6,362 real changes made)
{com}. replace rti=-0.82 if iscoco2==21
{txt}(2,027 real changes made)
{com}. replace rti=-1.00 if iscoco2==22
{txt}(1,464 real changes made)
{com}. replace rti=-0.73 if iscoco2==24
{txt}(3,113 real changes made)
{com}. replace rti=-1.52 if iscoco2==13
{txt}(1,383 real changes made)
{com}. replace rti=-0.40 if iscoco2==31
{txt}(1,492 real changes made)
{com}. replace rti=-0.44 if iscoco2==34
{txt}(3,500 real changes made)
{com}. replace rti=-0.33 if iscoco2==32
{txt}(2,939 real changes made)
{com}. 
. replace rti=0.32 if iscoco2==81
{txt}(505 real changes made)
{com}. replace rti=0.46 if iscoco2==72
{txt}(3,409 real changes made)
{com}. replace rti=-1.50 if iscoco2==83
{txt}(2,096 real changes made)
{com}. replace rti=2.24 if iscoco2==41
{txt}(7,305 real changes made)
{com}. replace rti=1.59 if iscoco2==73
{txt}(235 real changes made)
{com}. replace rti=-0.19 if iscoco2==71
{txt}(1,837 real changes made)
{com}. replace rti=1.41 if iscoco2==42
{txt}(2,000 real changes made)
{com}. replace rti=0.49 if iscoco2==82
{txt}(2,820 real changes made)
{com}. replace rti=1.24 if iscoco2==74
{txt}(602 real changes made)
{com}. 
. replace rti=0.45 if iscoco2==93
{txt}(1,639 real changes made)
{com}. replace rti=-0.60 if iscoco2==51
{txt}(5,200 real changes made)
{com}. replace rti=0.05 if iscoco2==52
{txt}(1,737 real changes made)
{com}. replace rti=0.03 if iscoco2==91
{txt}(2,876 real changes made)
{com}. {c )-}
. label var rti "RTI index"
. 
. {c )-}
. ************************************************************************
. ***** 2. Oesch (2006) and Kitschelt and Rehm (2014) classification *****
. ************************************************************************
. {c -(}
. *Now is the same, but using Oesch (2006) and Kitschelt and Rehm (2014) classification
. 
. */  Oesch (2006) develops the bases of a new class schema that partly shifts its focus from hierarchical divisions to horizontal cleavages. The idea is  that the middle class is not conceptualized as a unitary grouping and the manual/non-manual divide is not used as a decisive class boundary. 
. */ The emphasis is put on differences in marketable skills and the work logic. 
. 
. // What is the logic? 
. */Logic of task structures: t1 organizational (taskorg), t2 technical (tasktech), and t3 interpersonal (taskinter)
. */Logic of authority: a1 professional (authprof), a2 associate professional (authassoc), a3 skilled routine (authskil) a4 unskilled routine (authunsk)
. */Leads to 3*4 groups, in regressions of K&R combined to 4 groups (skilled+unskilled routine all tasks; prof+assoc prof for 3 tasks separately)
. 
. * Code from Thewissen and Rueda  (2019)
. {c -(}
. */t1a1: Higher grade managers
. gen t1a1=1 if iscoco>=1000 & iscoco<=1251 | iscoco>=2410 & iscoco<=2419 | inlist(iscoco,2441,2470)
{txt}(57,277 missing values generated)
{com}. label var t1a1 "Higher grade managers"
. 
. */t1a2: Associate managers
. gen t1a2=1 if iscoco>=1252 & iscoco<=1319 | iscoco>=3410 & iscoco<=3449 | inlist(iscoco,3452)
{txt}(60,438 missing values generated)
{com}. label var t1a2 "Associate managers"
. 
. */t1a3: Skilled office
. gen t1a3=1 if iscoco>=4000 & iscoco<=4112 | iscoco>=4114 & iscoco<=4141 | inlist(iscoco,4143) | iscoco>=4190 & iscoco<=4210 | iscoco>=4213 & iscoco<=4221
{txt}(57,961 missing values generated)
{com}. label var t1a3 "Skilled office"
. 
. */t1a4: Routine office
. gen t1a4=1 if inlist(iscoco,4113,4142,4144) | iscoco>=4211 & iscoco<=4212 | iscoco>=4222 & iscoco<=4223
{txt}(62,362 missing values generated)
{com}. label var t1a4 "Routine office"
. 
. */t2a1: Technical experts
. gen t2a1=1 if iscoco>=2100 & iscoco<=2213
{txt}(62,644 missing values generated)
{com}. label var t2a1 "Technical experts"
. 
. */t2a2: Technicians
. gen t2a2=1 if iscoco>=3100 & iscoco<=3213 | inlist(iscoco,3471)
{txt}(62,754 missing values generated)
{com}. label var t2a2 "Technicians"
. 
. */t2a3: Skilled crafts
. gen t2a3=1 if inlist(iscoco,110,8311,8324,8333) | iscoco>=7120 & iscoco<=7142 | iscoco>=7200 & iscoco<=7233 | iscoco>=7240 & iscoco<=7423 | iscoco>=7430 & iscoco<=7520
{txt}(57,658 missing values generated)
{com}. label var t2a3 "Skilled crafts"
. 
. */t2a4: Routine operatives/agriculture
. gen t2a4=1 if inlist(iscoco,7143, 7234, 7424, 8312) | iscoco>=7100 & iscoco<=7113 | iscoco>=7129 & iscoco<=7130 | iscoco>=8000 & iscoco<=8310 | iscoco>=8334 & iscoco<=8400 | iscoco>=9160 & iscoco<=9162 | iscoco>=9300 & iscoco<=9333
{txt}(59,418 missing values generated)
{com}. replace t2a4=1 if iscoco>=6010 & iscoco<=6210 | iscoco>=8330 & iscoco<=8332 | iscoco>=9200 & iscoco<=9213
{txt}(1,152 real changes made)
{com}. label var t2a4 "Routine operatives/agriculture"
. 
. */t3a1: Socio-cultural professionals
. gen t3a1=1 if inlist(iscoco, 2359, 2445, 2451, 2460) | iscoco>=2220 & iscoco<=2323 | iscoco>=2350 & iscoco<=2351 |iscoco>=2420 & iscoco<=2440 | iscoco>=2442 & iscoco<=2443
{txt}(60,951 missing values generated)
{com}. label var t3a1 "Socio-cultural professionals"
. 
. */t3a2: Socio-cultural semi-professionals
. gen t3a2=1 if inlist(iscoco,2352, 2444, 3220, 3226) | iscoco>=2330 & iscoco<=2340 | iscoco>=2446 & iscoco<=2450 | iscoco>=2452 & iscoco<=2455 | iscoco>=3222 & iscoco<=3224 | iscoco>=3229 & iscoco<=3232 | iscoco>=3240 & iscoco<=3400 | iscoco>=3450 & iscoco<=3451 | iscoco>=3460 & iscoco<=3470 | iscoco>=3472 & iscoco<=3480
{txt}(59,364 missing values generated)
{com}. label var t3a2 "Socio-cultural semi-professionals"
. 
. */t3a3: Skilled service
. gen t3a3=1 if inlist(iscoco, 3221, 3225, 5122, 5141, 5143, 8323) | iscoco>=3227 & iscoco<=3228 | iscoco>=5110 & iscoco<=5113 | iscoco>=5150 & iscoco<=5163 | iscoco>=5200 & iscoco<=5210
{txt}(61,772 missing values generated)
{com}. label var t3a3 "Skilled service"
. 
. */t3a4: Routine service
. gen t3a4=1 if inlist(iscoco,5142, 5149, 5169) | iscoco>=5120 & iscoco<=5121 | iscoco>=5123 & iscoco<=5130 | iscoco>=5131 & iscoco<=5140 | iscoco>=5220 & iscoco<=5230 | iscoco>=8320 & iscoco<=8322 | iscoco>=9100 & iscoco<=9153
{txt}(56,203 missing values generated)
{com}. label var t3a4 "Routine service"
. 
. 
. forvalues i=1(1)3 {c -(}
{txt}  2{com}.         gen t`i'=1 if t`i'a1==1 | t`i'a2==1  | t`i'a3==1 | t`i'a4==1
{txt}  3{com}.         {c )-}
{txt}(43,596 missing values generated)
(46,981 missing values generated)
(43,848 missing values generated)
{com}. list t1-t3 if t1==t2 & t2==t3 & t1==t3 & t1~=.
. replace t1=0 if t2==1 | t3==1
{txt}(38,799 real changes made)
{com}. replace t2=0 if t1==1 | t3==1
{txt}(42,184 real changes made)
{com}. replace t3=0 if t1==1 | t2==1
{txt}(39,051 real changes made)
{com}. 
. label var t1 "Organisational task structure (t1a1, 2, 3, or 4==1)"
. label var t2 "Technical task structure (t2a1, 2, 3, or 4==1)"
. label var t3 "Interpersonal task structure (t3a1, 2, 3, or 4==1)"
. 
. forvalues i=1(1)4 {c -(}
{txt}  2{com}.         gen a`i'=1 if t1a`i'==1 | t2a`i'==1  | t3a`i'==1
{txt}  3{com}.         {c )-}
{txt}(51,244 missing values generated)
(52,928 missing values generated)
(47,763 missing values generated)
(47,203 missing values generated)
{com}. replace a1=0 if a2==1 | a3==1 | a4==1
{txt}(46,447 real changes made)
{com}. replace a2=0 if a1==1 | a3==1 | a4==1
{txt}(48,131 real changes made)
{com}. replace a3=0 if a1==1 | a2==1 | a4==1
{txt}(43,067 real changes made)
{com}. replace a4=0 if a1==1 | a2==1 | a3==1
{txt}(42,406 real changes made)
{com}. 
. label var a1 "Professional authority (t1a1, t2a1, or t3a1==1)"
. label var a2 "Assoc prof authority (t1a2, t2a2, or t3a2==1)"
. label var a3 "Skilled routine authority (t1a3, t2a3, or t3a3==1)"
. label var a4 "Unskilled routine authority (t1a4, t2a4, or t3a4==1)"
. 
. gen check = a1+a2+a3+a4
{txt}(4,797 missing values generated)
{com}. sum check

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}check {c |}{res}     60,017           1           0          1          1
{com}. drop check
. 
. gen c1=1 if t1a1==1 | t1a2==1
{txt}(52,901 missing values generated)
{com}. gen c2=1 if t2a1==1 | t2a2==1
{txt}(60,584 missing values generated)
{com}. gen c3=1 if t3a1==1 | t3a2==1
{txt}(55,501 missing values generated)
{com}. gen c4=1 if t1a3==1 | t1a4==1 | t2a3==1 | t2a4==1 | t3a3==1 | t3a4==1
{txt}(30,253 missing values generated)
{com}. 
. replace c1=0 if c2==1 | c3==1 | c4==1
{txt}(48,104 real changes made)
{com}. replace c2=0 if c1==1 | c3==1 | c4==1
{txt}(55,787 real changes made)
{com}. replace c3=0 if c1==1 | c2==1 | c4==1
{txt}(50,704 real changes made)
{com}. replace c4=0 if c1==1 | c2==1 | c3==1
{txt}(25,456 real changes made)
{com}. 
. label var c1 "Skilled organisational (t1a1 or t1a2==1)"
. label var c2 "Skilled technical (t2a1 or t2a2==1)"
. label var c3 "Skilled interpersonal (t3a1 or t3a2==1)"
. label var c4 "Unskilled routine (t1a3, t1a4, t2a3, t2a4, t3a3, or t3a4==1)"
. 
. gen check = c1+c2+c3+c4
{txt}(4,797 missing values generated)
{com}. sum check

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}check {c |}{res}     60,017           1           0          1          1
{com}. drop check
. {c )-}
. {c )-}
. *********************************************************************
. ***** 3. Offshoring index from Walter (2017), based on Blinder*******
. *********************************************************************
. {c -(}
. */ Stefanie Walter (2017) provides the code for offshorability based on Blinder. It uses ISCO with 4 digits. 
. 
. gen offshwalt=.
{txt}(64,814 missing values generated)
{com}. label var offshwalt "Offshoring Potential (Blinder) from Walter"
. 
. 
. *4-digit ISCO-code
.  * Coding is based on the classification developed in Blinder, Alan. 2007. "How Many U.S. Jobs Might Be Offshorable." CEPS Working Paper No. 142.
. * NOTE: all professions not listed by Blinder are coded as not offshorable (value 0)
. * Unlike Goos et al, Walter considers the 4 digits. 
. {c -(}
. replace offshwalt=0 if iscoco<.
{txt}(60,912 real changes made)
{com}. replace offshwalt=49 if iscoco==1142
{txt}(0 real changes made)
{com}. replace offshwalt=55 if iscoco==1222
{txt}(0 real changes made)
{com}. replace offshwalt=28 if iscoco==1226
{txt}(132 real changes made)
{com}. replace offshwalt=55 if iscoco==1228
{txt}(0 real changes made)
{com}. replace offshwalt=83 if iscoco==1231
{txt}(385 real changes made)
{com}. replace offshwalt=49 if iscoco==1232
{txt}(98 real changes made)
{com}. replace offshwalt=40 if iscoco==1233
{txt}(1,794 real changes made)
{com}. replace offshwalt=53 if iscoco==1234
{txt}(63 real changes made)
{com}. replace offshwalt=49 if iscoco==1235
{txt}(152 real changes made)
{com}. replace offshwalt=55 if iscoco==1236
{txt}(92 real changes made)
{com}. replace offshwalt=55 if iscoco==1237
{txt}(5 real changes made)
{com}. replace offshwalt=55 if iscoco==1311
{txt}(27 real changes made)
{com}. replace offshwalt=55 if iscoco==1312
{txt}(0 real changes made)
{com}. replace offshwalt=55 if iscoco==1313
{txt}(280 real changes made)
{com}. replace offshwalt=55 if iscoco==1314
{txt}(0 real changes made)
{com}. replace offshwalt=55 if iscoco==1315
{txt}(57 real changes made)
{com}. replace offshwalt=48 if iscoco==1316
{txt}(0 real changes made)
{com}. replace offshwalt=52 if iscoco==1317
{txt}(0 real changes made)
{com}. replace offshwalt=55 if iscoco==1318
{txt}(0 real changes made)
{com}. replace offshwalt=55 if iscoco==1319
{txt}(1,019 real changes made)
{com}. replace offshwalt=66 if iscoco==2111
{txt}(6 real changes made)
{com}. replace offshwalt=74 if iscoco==2112
{txt}(2 real changes made)
{com}. replace offshwalt=66 if iscoco==2113
{txt}(56 real changes made)
{com}. replace offshwalt=66 if iscoco==2114
{txt}(0 real changes made)
{com}. replace offshwalt=89 if iscoco==2121
{txt}(44 real changes made)
{com}. replace offshwalt=96 if iscoco==2122
{txt}(10 real changes made)
{com}. replace offshwalt=83 if iscoco==2131
{txt}(614 real changes made)
{com}. replace offshwalt=90 if iscoco==2139
{txt}(0 real changes made)
{com}. replace offshwalt=25 if iscoco==2141
{txt}(162 real changes made)
{com}. replace offshwalt=64 if iscoco==2143
{txt}(160 real changes made)
{com}. replace offshwalt=74 if iscoco==2144
{txt}(51 real changes made)
{com}. replace offshwalt=72 if iscoco==2146
{txt}(38 real changes made)
{com}. replace offshwalt=69 if iscoco==2147
{txt}(23 real changes made)
{com}. replace offshwalt=86 if iscoco==2148
{txt}(18 real changes made)
{com}. replace offshwalt=71 if iscoco==2149
{txt}(383 real changes made)
{com}. replace offshwalt=81 if iscoco==2211
{txt}(108 real changes made)
{com}. replace offshwalt=83 if iscoco==2212
{txt}(1 real change made)
{com}. replace offshwalt=72 if iscoco==2411
{txt}(602 real changes made)
{com}. replace offshwalt=50 if iscoco==2419
{txt}(260 real changes made)
{com}. replace offshwalt=74 if iscoco==2421
{txt}(285 real changes made)
{com}. replace offshwalt=67 if iscoco==2444
{txt}(26 real changes made)
{com}. replace offshwalt=90 if iscoco==2451
{txt}(210 real changes made)
{com}. replace offshwalt=78 if iscoco==2452
{txt}(134 real changes made)
{com}. replace offshwalt=25 if iscoco==2453
{txt}(103 real changes made)
{com}. replace offshwalt=48 if iscoco==2455
{txt}(85 real changes made)
{com}. replace offshwalt=47 if iscoco==3111
{txt}(66 real changes made)
{com}. replace offshwalt=47 if iscoco==3113
{txt}(0 real changes made)
{com}. replace offshwalt=47 if iscoco==3114
{txt}(2 real changes made)
{com}. replace offshwalt=72 if iscoco==3115
{txt}(11 real changes made)
{com}. replace offshwalt=47 if iscoco==3116
{txt}(0 real changes made)
{com}. replace offshwalt=94 if iscoco==3118
{txt}(95 real changes made)
{com}. replace offshwalt=54 if iscoco==3119
{txt}(0 real changes made)
{com}. replace offshwalt=75 if iscoco==3121
{txt}(119 real changes made)
{com}. replace offshwalt=84 if iscoco==3122
{txt}(118 real changes made)
{com}. replace offshwalt=68 if iscoco==3123
{txt}(0 real changes made)
{com}. replace offshwalt=36 if iscoco==3131
{txt}(80 real changes made)
{com}. replace offshwalt=36 if iscoco==3132
{txt}(53 real changes made)
{com}. replace offshwalt=46 if iscoco==3133
{txt}(113 real changes made)
{com}. replace offshwalt=34 if iscoco==3139
{txt}(0 real changes made)
{com}. replace offshwalt=52 if iscoco==3141
{txt}(3 real changes made)
{com}. replace offshwalt=60 if iscoco==3152
{txt}(536 real changes made)
{com}. replace offshwalt=55 if iscoco==3211
{txt}(281 real changes made)
{com}. replace offshwalt=55 if iscoco==3212
{txt}(20 real changes made)
{com}. replace offshwalt=44 if iscoco==3213
{txt}(0 real changes made)
{com}. replace offshwalt=32 if iscoco==3228
{txt}(0 real changes made)
{com}. replace offshwalt=51 if iscoco==3411
{txt}(96 real changes made)
{com}. replace offshwalt=85 if iscoco==3412
{txt}(289 real changes made)
{com}. replace offshwalt=50 if iscoco==3414
{txt}(0 real changes made)
{com}. replace offshwalt=55 if iscoco==3416
{txt}(200 real changes made)
{com}. replace offshwalt=59 if iscoco==3419
{txt}(0 real changes made)
{com}. replace offshwalt=51 if iscoco==3421
{txt}(0 real changes made)
{com}. replace offshwalt=48 if iscoco==3422
{txt}(8 real changes made)
{com}. replace offshwalt=38 if iscoco==3431
{txt}(0 real changes made)
{com}. replace offshwalt=51 if iscoco==3432
{txt}(423 real changes made)
{com}. replace offshwalt=84 if iscoco==3433
{txt}(0 real changes made)
{com}. replace offshwalt=84 if iscoco==3434
{txt}(25 real changes made)
{com}. replace offshwalt=54 if iscoco==3439
{txt}(0 real changes made)
{com}. replace offshwalt=100 if iscoco==3442
{txt}(43 real changes made)
{com}. replace offshwalt=85 if iscoco==3471
{txt}(267 real changes made)
{com}. replace offshwalt=30 if iscoco==3472
{txt}(21 real changes made)
{com}. replace offshwalt=95 if iscoco==4111
{txt}(525 real changes made)
{com}. replace offshwalt=94 if iscoco==4112
{txt}(0 real changes made)
{com}. replace offshwalt=100 if iscoco==4113
{txt}(223 real changes made)
{com}. replace offshwalt=71 if iscoco==4114
{txt}(187 real changes made)
{com}. replace offshwalt=38 if iscoco==4115
{txt}(2,563 real changes made)
{com}. replace offshwalt=84 if iscoco==4121
{txt}(1,043 real changes made)
{com}. replace offshwalt=54 if iscoco==4122
{txt}(121 real changes made)
{com}. replace offshwalt=31 if iscoco==4131
{txt}(804 real changes made)
{com}. replace offshwalt=67 if iscoco==4132
{txt}(137 real changes made)
{com}. replace offshwalt=67 if iscoco==4133
{txt}(103 real changes made)
{com}. replace offshwalt=46 if iscoco==4141
{txt}(185 real changes made)
{com}. replace offshwalt=26 if iscoco==4142
{txt}(391 real changes made)
{com}. replace offshwalt=95 if iscoco==4143
{txt}(17 real changes made)
{com}. replace offshwalt=54 if iscoco==4144
{txt}(0 real changes made)
{com}. replace offshwalt=54 if iscoco==4190
{txt}(337 real changes made)
{com}. replace offshwalt=94 if iscoco==4211
{txt}(957 real changes made)
{com}. replace offshwalt=54 if iscoco==4214
{txt}(0 real changes made)
{com}. replace offshwalt=65 if iscoco==4215
{txt}(54 real changes made)
{com}. replace offshwalt=72 if iscoco==4221
{txt}(60 real changes made)
{com}. replace offshwalt=54 if iscoco==4222
{txt}(422 real changes made)
{com}. replace offshwalt=50 if iscoco==4223
{txt}(252 real changes made)
{com}. replace offshwalt=86 if iscoco==5113
{txt}(10 real changes made)
{com}. replace offshwalt=59 if iscoco==6142
{txt}(0 real changes made)
{com}. replace offshwalt=36 if iscoco==7111
{txt}(12 real changes made)
{com}. replace offshwalt=35 if iscoco==7112
{txt}(13 real changes made)
{com}. replace offshwalt=36 if iscoco==7113
{txt}(0 real changes made)
{com}. replace offshwalt=65 if iscoco==7211
{txt}(0 real changes made)
{com}. replace offshwalt=69 if iscoco==7212
{txt}(342 real changes made)
{com}. replace offshwalt=70 if iscoco==7213
{txt}(94 real changes made)
{com}. replace offshwalt=70 if iscoco==7222
{txt}(318 real changes made)
{com}. replace offshwalt=68 if iscoco==7223
{txt}(87 real changes made)
{com}. replace offshwalt=68 if iscoco==7224
{txt}(8 real changes made)
{com}. replace offshwalt=26 if iscoco==7311
{txt}(32 real changes made)
{com}. replace offshwalt=64 if iscoco==7313
{txt}(28 real changes made)
{com}. replace offshwalt=69 if iscoco==7321
{txt}(0 real changes made)
{com}. replace offshwalt=69 if iscoco==7322
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==7323
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==7324
{txt}(94 real changes made)
{com}. replace offshwalt=60 if iscoco==7331
{txt}(0 real changes made)
{com}. replace offshwalt=75 if iscoco==7332
{txt}(0 real changes made)
{com}. replace offshwalt=59 if iscoco==7341
{txt}(57 real changes made)
{com}. replace offshwalt=59 if iscoco==7342
{txt}(0 real changes made)
{com}. replace offshwalt=59 if iscoco==7343
{txt}(7 real changes made)
{com}. replace offshwalt=34 if iscoco==7344
{txt}(0 real changes made)
{com}. replace offshwalt=59 if iscoco==7345
{txt}(0 real changes made)
{com}. replace offshwalt=75 if iscoco==7346
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==7413
{txt}(0 real changes made)
{com}. replace offshwalt=27 if iscoco==7414
{txt}(0 real changes made)
{com}. replace offshwalt=55 if iscoco==7415
{txt}(44 real changes made)
{com}. replace offshwalt=43 if iscoco==7421
{txt}(0 real changes made)
{com}. replace offshwalt=57 if iscoco==7422
{txt}(72 real changes made)
{com}. replace offshwalt=57 if iscoco==7423
{txt}(0 real changes made)
{com}. replace offshwalt=66 if iscoco==7424
{txt}(0 real changes made)
{com}. replace offshwalt=75 if iscoco==7431
{txt}(0 real changes made)
{com}. replace offshwalt=75 if iscoco==7432
{txt}(0 real changes made)
{com}. replace offshwalt=75 if iscoco==7433
{txt}(128 real changes made)
{com}. replace offshwalt=73 if iscoco==7434
{txt}(0 real changes made)
{com}. replace offshwalt=75 if iscoco==7435
{txt}(64 real changes made)
{com}. replace offshwalt=75 if iscoco==7436
{txt}(0 real changes made)
{com}. replace offshwalt=57 if iscoco==7437
{txt}(39 real changes made)
{com}. replace offshwalt=75 if iscoco==7441
{txt}(0 real changes made)
{com}. replace offshwalt=72 if iscoco==7442
{txt}(16 real changes made)
{com}. replace offshwalt=36 if iscoco==8111
{txt}(69 real changes made)
{com}. replace offshwalt=36 if iscoco==8112
{txt}(0 real changes made)
{com}. replace offshwalt=36 if iscoco==8113
{txt}(30 real changes made)
{com}. replace offshwalt=59 if iscoco==8121
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8122
{txt}(39 real changes made)
{com}. replace offshwalt=70 if iscoco==8123
{txt}(10 real changes made)
{com}. replace offshwalt=68 if iscoco==8124
{txt}(0 real changes made)
{com}. replace offshwalt=69 if iscoco==8131
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8139
{txt}(0 real changes made)
{com}. replace offshwalt=57 if iscoco==8141
{txt}(77 real changes made)
{com}. replace offshwalt=68 if iscoco==8142
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8143
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8151
{txt}(61 real changes made)
{com}. replace offshwalt=70 if iscoco==8152
{txt}(51 real changes made)
{com}. replace offshwalt=68 if iscoco==8153
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8154
{txt}(0 real changes made)
{com}. replace offshwalt=29 if iscoco==8155
{txt}(17 real changes made)
{com}. replace offshwalt=68 if iscoco==8159
{txt}(0 real changes made)
{com}. replace offshwalt=42 if iscoco==8161
{txt}(14 real changes made)
{com}. replace offshwalt=55 if iscoco==8162
{txt}(78 real changes made)
{com}. replace offshwalt=29 if iscoco==8163
{txt}(27 real changes made)
{com}. replace offshwalt=64 if iscoco==8171
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8172
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8211
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8212
{txt}(13 real changes made)
{com}. replace offshwalt=68 if iscoco==8221
{txt}(41 real changes made)
{com}. replace offshwalt=35 if iscoco==8222
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8223
{txt}(8 real changes made)
{com}. replace offshwalt=41 if iscoco==8224
{txt}(42 real changes made)
{com}. replace offshwalt=29 if iscoco==8229
{txt}(0 real changes made)
{com}. replace offshwalt=69 if iscoco==8231
{txt}(11 real changes made)
{com}. replace offshwalt=68 if iscoco==8232
{txt}(186 real changes made)
{com}. replace offshwalt=57 if iscoco==8240
{txt}(0 real changes made)
{com}. replace offshwalt=58 if iscoco==8251
{txt}(209 real changes made)
{com}. replace offshwalt=59 if iscoco==8252
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8253
{txt}(33 real changes made)
{com}. replace offshwalt=75 if iscoco==8261
{txt}(81 real changes made)
{com}. replace offshwalt=75 if iscoco==8262
{txt}(52 real changes made)
{com}. replace offshwalt=75 if iscoco==8263
{txt}(561 real changes made)
{com}. replace offshwalt=75 if iscoco==8264
{txt}(234 real changes made)
{com}. replace offshwalt=75 if iscoco==8265
{txt}(0 real changes made)
{com}. replace offshwalt=75 if iscoco==8266
{txt}(47 real changes made)
{com}. replace offshwalt=75 if iscoco==8269
{txt}(71 real changes made)
{com}. replace offshwalt=27 if iscoco==8271
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8272
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8273
{txt}(0 real changes made)
{com}. replace offshwalt=30 if iscoco==8274
{txt}(0 real changes made)
{com}. replace offshwalt=31 if iscoco==8275
{txt}(15 real changes made)
{com}. replace offshwalt=68 if iscoco==8276
{txt}(0 real changes made)
{com}. replace offshwalt=27 if iscoco==8277
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8278
{txt}(0 real changes made)
{com}. replace offshwalt=66 if iscoco==8281
{txt}(0 real changes made)
{com}. replace offshwalt=66 if iscoco==8282
{txt}(219 real changes made)
{com}. replace offshwalt=66 if iscoco==8283
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8284
{txt}(0 real changes made)
{com}. replace offshwalt=57 if iscoco==8285
{txt}(0 real changes made)
{com}. replace offshwalt=64 if iscoco==8286
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8290
{txt}(216 real changes made)
{com}. replace offshwalt=34 if iscoco==8340
{txt}(15 real changes made)
{com}. replace offshwalt=95 if iscoco==9113
{txt}(62 real changes made)
{com}. replace offshwalt=64 if iscoco==9321
{txt}(49 real changes made)
{com}. replace offshwalt=29 if iscoco==9333
{txt}(675 real changes made)
{com}. replace offshwalt=55 if iscoco==1227
{txt}(753 real changes made)
{com}. replace offshwalt=89 if iscoco==2121
{txt}(0 real changes made)
{com}. replace offshwalt=100 if iscoco==2132
{txt}(156 real changes made)
{com}. replace offshwalt=70 if iscoco==2145
{txt}(149 real changes made)
{com}. replace offshwalt=71 if iscoco==2149
{txt}(0 real changes made)
{com}. replace offshwalt=81 if iscoco==2211
{txt}(0 real changes made)
{com}. replace offshwalt=46 if iscoco==2412
{txt}(290 real changes made)
{com}. replace offshwalt=50 if iscoco==2419
{txt}(0 real changes made)
{com}. replace offshwalt=33 if iscoco==2432
{txt}(162 real changes made)
{com}. replace offshwalt=89 if iscoco==2441
{txt}(14 real changes made)
{com}. replace offshwalt=48 if iscoco==2455
{txt}(0 real changes made)
{com}. replace offshwalt=72 if iscoco==3115
{txt}(0 real changes made)
{com}. replace offshwalt=54 if iscoco==3119
{txt}(0 real changes made)
{com}. replace offshwalt=46 if iscoco==3133
{txt}(0 real changes made)
{com}. replace offshwalt=34 if iscoco==3139
{txt}(0 real changes made)
{com}. replace offshwalt=34 if iscoco==3224
{txt}(39 real changes made)
{com}. replace offshwalt=51 if iscoco==3411
{txt}(0 real changes made)
{com}. replace offshwalt=25 if iscoco==3415
{txt}(1,019 real changes made)
{com}. replace offshwalt=50 if iscoco==3417
{txt}(154 real changes made)
{com}. replace offshwalt=59 if iscoco==3419
{txt}(0 real changes made)
{com}. replace offshwalt=51 if iscoco==3432
{txt}(0 real changes made)
{com}. replace offshwalt=54 if iscoco==3439
{txt}(0 real changes made)
{com}. replace offshwalt=90 if iscoco==3460
{txt}(30 real changes made)
{com}. replace offshwalt=54 if iscoco==4122
{txt}(0 real changes made)
{com}. replace offshwalt=31 if iscoco==4131
{txt}(0 real changes made)
{com}. replace offshwalt=67 if iscoco==4132
{txt}(0 real changes made)
{com}. replace offshwalt=46 if iscoco==4141
{txt}(0 real changes made)
{com}. replace offshwalt=26 if iscoco==4142
{txt}(0 real changes made)
{com}. replace offshwalt=54 if iscoco==4222
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==7141
{txt}(190 real changes made)
{com}. replace offshwalt=68 if iscoco==7224
{txt}(0 real changes made)
{com}. replace offshwalt=65 if iscoco==7241
{txt}(66 real changes made)
{com}. replace offshwalt=34 if iscoco==7344
{txt}(0 real changes made)
{com}. replace offshwalt=72 if iscoco==7442
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8122
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8139
{txt}(0 real changes made)
{com}. replace offshwalt=42 if iscoco==8161
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8211
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8223
{txt}(0 real changes made)
{com}. replace offshwalt=41 if iscoco==8224
{txt}(0 real changes made)
{com}. replace offshwalt=57 if iscoco==8240
{txt}(0 real changes made)
{com}. replace offshwalt=58 if iscoco==8251
{txt}(0 real changes made)
{com}. replace offshwalt=75 if iscoco==8269
{txt}(0 real changes made)
{com}. replace offshwalt=66 if iscoco==8281
{txt}(0 real changes made)
{com}. replace offshwalt=66 if iscoco==8283
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8284
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8290
{txt}(0 real changes made)
{com}. replace offshwalt=70 if iscoco==9322
{txt}(251 real changes made)
{com}. {c )-}
. 
. * Create Ordinal Offshwalt Variable
. {c -(}
. gen offshwalt4=.
{txt}(64,814 missing values generated)
{com}. replace offshwalt4=4 if offshwalt<.
{txt}(60,912 real changes made)
{com}. replace offshwalt4=3 if offshwalt<75
{txt}(54,157 real changes made)
{com}. replace offshwalt4=2 if offshwalt<50
{txt}(43,409 real changes made)
{com}. replace offshwalt4=1 if offshwalt<25
{txt}(34,126 real changes made)
{com}. label var offshwalt4 "offshwalt ordinal (4 categories)"
. {c )-}
. 
. * Binary Offshwalt Variable
. gen offshwalt2=offshwalt4>1
. replace offshwalt2=. if offshwalt==.
{txt}(3,902 real changes made, 3,902 to missing)
{com}. label var offshwalt2 "offshwalt binary: =1 if offshwalt>=25 if offshwalt"
. {c )-}
. *******************************************************************************
. *****4. Skill specificity provided Iversen, Cusack and Rehm (2011) ************
. *******************************************************************************
. {c -(}
. {c -(}
. */ This code is provided by Torben Iversen; Thomas Cusack; Philipp Rehm, 2011: Risks at Work The Demand and Supply Sides of Government Redistribution
. {c -(}
. gen relskillspec=.      
{txt}(64,814 missing values generated)
{com}.         replace relskillspec=6.168528055        if iscoco2==11
{txt}(469 real changes made)
{com}.         replace relskillspec=2.840445869        if iscoco2==12
{txt}(6,362 real changes made)
{com}.         replace relskillspec=1.612754672        if iscoco2==13
{txt}(1,383 real changes made)
{com}.         replace relskillspec=3.94911483         if iscoco2==21
{txt}(2,027 real changes made)
{com}.         replace relskillspec=2.875304662        if iscoco2==22
{txt}(1,464 real changes made)
{com}.         replace relskillspec=1.298205499        if iscoco2==23
{txt}(3,200 real changes made)
{com}.         replace relskillspec=3.38271403         if iscoco2==24
{txt}(3,113 real changes made)
{com}.         replace relskillspec=5.999693379        if iscoco2==31
{txt}(1,492 real changes made)
{com}.         replace relskillspec=5.599717306        if iscoco2==32
{txt}(2,939 real changes made)
{com}.         replace relskillspec=2.435374975        if iscoco2==33
{txt}(365 real changes made)
{com}.         replace relskillspec=3.582262408        if iscoco2==34
{txt}(3,500 real changes made)
{com}.         replace relskillspec=1.803739154        if iscoco2==41
{txt}(7,305 real changes made)
{com}.         replace relskillspec=3.899735492        if iscoco2==42
{txt}(2,000 real changes made)
{com}.         replace relskillspec=3.020082744        if iscoco2==51
{txt}(5,200 real changes made)
{com}.         replace relskillspec=0.787535801        if iscoco2==52
{txt}(1,737 real changes made)
{com}.         replace relskillspec=4.674256488        if iscoco2==61
{txt}(490 real changes made)
{com}.         replace relskillspec=3.91546829         if iscoco2==71
{txt}(1,837 real changes made)
{com}.         replace relskillspec=3.842807254        if iscoco2==72
{txt}(3,409 real changes made)
{com}.         replace relskillspec=20.41910075        if iscoco2==73
{txt}(235 real changes made)
{com}.         replace relskillspec=9.446026838        if iscoco2==74
{txt}(602 real changes made)
{com}.         replace relskillspec=25.06473567        if iscoco2==81
{txt}(505 real changes made)
{com}.         replace relskillspec=12.27676336        if iscoco2==82
{txt}(2,820 real changes made)
{com}.         replace relskillspec=3.672294237        if iscoco2==83
{txt}(2,096 real changes made)
{com}.         replace relskillspec=7.39344664         if iscoco2==91
{txt}(2,876 real changes made)
{com}.         replace relskillspec=7.384150506        if iscoco2==92
{txt}(435 real changes made)
{com}.         replace relskillspec=6.460338111        if iscoco2==93
{txt}(1,639 real changes made)
{com}.         label var relskillspec "Relative skill specificity , Iversen"
. {c )-}
.         
. {c )-}
. 
. {c )-}
. *******************************************************************************
. * 5. task categories Kurer (2020)
. *******************************************************************************
. {c -(}
. rename iscoco iscoco_withoutchange
{res}{com}. gen iscoco=iscoco_withoutchange
{txt}(3,902 missing values generated)
{com}.     
. {c -(}
. *generating 3 task categories
. gen task = .
{txt}(64,814 missing values generated)
{com}. gen isco=iscoco
{txt}(3,902 missing values generated)
{com}. replace task = 1 if inlist(isco, 2411, 2431, 2441, 3411, 3471)
{txt}(1,004 real changes made)
{com}. replace task = 1 if inrange(isco, 2100, 2213)
{txt}(2,170 real changes made)
{com}. replace task = 1 if inrange(isco, 2443, 2444)
{txt}(26 real changes made)
{com}. replace task = 1 if inrange(isco, 2446, 2452)
{txt}(984 real changes made)
{com}. replace task = 1 if inrange(isco, 3100, 3212)
{txt}(1,793 real changes made)
{com}. replace task = 1 if inrange(isco, 3433, 3440)
{txt}(25 real changes made)
{com}. replace task = 1 if inrange(isco, 3442, 3444)
{txt}(65 real changes made)
{com}. 
. replace task = 2 if inlist(isco, 2442, 2445, 3226, 3432, 3441)
{txt}(806 real changes made)
{com}. replace task = 2 if inrange(isco, 1000, 1319)
{txt}(7,754 real changes made)
{com}. replace task = 2 if inrange(isco, 2220, 2410)
{txt}(4,521 real changes made)
{com}. replace task = 2 if inrange(isco, 2412, 2430)
{txt}(861 real changes made)
{com}. replace task = 2 if inrange(isco, 2432, 2440)
{txt}(180 real changes made)
{com}. replace task = 2 if inrange(isco, 2453, 2470)
{txt}(353 real changes made)
{com}. replace task = 2 if inrange(isco, 3213, 3220)
{txt}(0 real changes made)
{com}. replace task = 2 if inrange(isco, 3222, 3224)
{txt}(91 real changes made)
{com}. replace task = 2 if inrange(isco, 3229, 3410)
{txt}(2,369 real changes made)
{com}. replace task = 2 if inrange(isco, 3412, 3429)
{txt}(2,219 real changes made)
{com}. replace task = 2 if inrange(isco, 3449, 3470)
{txt}(125 real changes made)
{com}. replace task = 2 if inrange(isco, 3472, 3480)
{txt}(110 real changes made)
{com}. 
. replace task = 3 if isco==4223
{txt}(252 real changes made)
{com}. replace task = 3 if inrange(isco, 3430, 3431)
{txt}(0 real changes made)
{com}. replace task = 3 if inrange(isco, 4000, 4195)
{txt}(7,305 real changes made)
{com}. replace task = 3 if inrange(isco, 4210, 4215)
{txt}(1,266 real changes made)
{com}. 
. replace task = 4 if inlist(isco, 7124, 8340, 9120, 9133)
{txt}(620 real changes made)
{com}. replace task = 4 if inrange(isco, 1, 110) /* departing from oesch, including 110 (armed forces). this is the actual intention of 1-100 */
{txt}(460 real changes made)
{com}. replace task = 4 if inrange(isco, 6100, 7113)
{txt}(515 real changes made)
{com}. replace task = 4 if inrange(isco, 7210, 8290)
{txt}(7,516 real changes made)
{com}. replace task = 4 if inrange(isco, 9000, 9001)
{txt}(0 real changes made)
{com}. replace task = 4 if inrange(isco, 9150, 9151)
{txt}(139 real changes made)
{com}. replace task = 4 if inrange(isco, 9153, 9161)
{txt}(86 real changes made)
{com}. replace task = 4 if inrange(isco, 9200, 9311)
{txt}(471 real changes made)
{com}. 
. replace task = 5 if inlist(isco, 5122, 5143, 9002, 9162)
{txt}(1,088 real changes made)
{com}. replace task = 5 if inrange(isco, 7120, 7123)
{txt}(141 real changes made)
{com}. replace task = 5 if inrange(isco, 7129, 7143)
{txt}(1,066 real changes made)
{com}. replace task = 5 if inrange(isco, 8300, 8334)
{txt}(2,081 real changes made)
{com}. replace task = 5 if inrange(isco, 9130, 9132)
{txt}(1,235 real changes made)
{com}. replace task = 5 if inrange(isco, 9140, 9142)
{txt}(1,160 real changes made)
{com}. replace task = 5 if inrange(isco, 9312, 9313)
{txt}(601 real changes made)
{com}. 
. replace task = 6 if inlist(isco, 3221, 3225, 4200, 9152)
{txt}(474 real changes made)
{com}. replace task = 6 if inrange(isco, 3227, 3228)
{txt}(18 real changes made)
{com}. replace task = 6 if inrange(isco, 4220, 4222)
{txt}(482 real changes made)
{com}. replace task = 6 if inrange(isco, 5000, 5121)
{txt}(237 real changes made)
{com}. replace task = 6 if inrange(isco, 5123, 5142)
{txt}(2,984 real changes made)
{com}. replace task = 6 if inrange(isco, 5149, 5220)
{txt}(2,628 real changes made)
{com}. replace task = 6 if inrange(isco, 9003, 9005)
{txt}(0 real changes made)
{com}. replace task = 6 if inrange(isco, 9100, 9113)
{txt}(162 real changes made)
{com}. replace task = 6 if inrange(isco, 9320, 9333) /* departing from oesch, including 9333 (transport labourers, animal vehicles) */
{txt}(1,002 real changes made)
{com}. 
. * add isco categories 2000 and 3000
. * officially not defined, thus not part of oesch's categories
. 
. replace task = 1 if inlist(isco, 2000, 3000)
{txt}(0 real changes made)
{com}. 
. * task3
. 
. gen task3 = .
{txt}(64,814 missing values generated)
{com}. replace task3 = 1 if task==1 | task==2 // NRM
{txt}(25,456 real changes made)
{com}. replace task3 = 2 if task==3 | task==4 // R
{txt}(18,630 real changes made)
{com}. replace task3 = 3 if task==5 | task==6 // NRC
{txt}(15,359 real changes made)
{com}. tab task3

      {txt}task3 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}     25,456       42.82       42.82
{txt}          2 {c |}{res}     18,630       31.34       74.16
{txt}          3 {c |}{res}     15,359       25.84      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     59,445      100.00
{com}. 
. 
. drop isco iscoco
. rename iscoco_withoutchange iscoco
{res}{com}. 
. {c )-}
. 
. {c )-}
. {c )-}
. 
. ************************************************************************
. * Generate a dummy to summarize the risk of automation 
. gen task3cog1=(task3==1) // dummy for non routine cognitive work - 3 task approach
. replace task3cog1=. if task3==.
{txt}(5,369 real changes made, 5,369 to missing)
{com}. gen task3cog2and3=(task3==2 | task3==3) // dummy for  routine cognitive work - 3 task approach
. replace task3cog2and3=. if task3==.
{txt}(5,369 real changes made, 5,369 to missing)
{com}. {c )-}
. 
. ************************************************************************
. ********************* D. Switching *************************************
. ************************************************************************
. {c -(}
. * Here I am looking at the answer in 2014 and then comparing with the answer in 2018
. // Note: 2014 and 2018 are the first years that the question about presidential election was incldued. THat is pres12 appeared for the first time in 2014 and pres16 in 2018
. gen vot_12_14=pres12 if year==2014
{txt}(63,215 missing values generated)
{com}. replace vot_12_14=. if pres12~=1 & pres12~=2 & pres12~=3 
{txt}(953 real changes made, 953 to missing)
{com}. by id, sort: egen vot12_14=total(vot_12_14)
. 
. 
. // This is my main definition of swithcing which is basically only 1 when change, but 0 in all the other cases
. gen switching2 = . 
{txt}(64,814 missing values generated)
{com}. replace switching2 = 1 if vot12_14==1 & pres16==2 // Democrat to Rep
{txt}(207 real changes made)
{com}. replace switching2 = 0 if vot12_14==1 & pres16==1 // Democrat to Democrat
{txt}(283 real changes made)
{com}. replace switching2 = 0 if vot12_14==2 //Republicans in 2012
{txt}(13,698 real changes made)
{com}. replace switching2 = 0 if vot12_14==1 & pres16~=2 // Democrat to anything except Rep
{txt}(22,679 real changes made)
{com}. replace switching2 = 0 if vot12_14==3 // Other candidate
{txt}(1,016 real changes made)
{com}. 
. rename switching2 switching2_broad 
{res}{com}. 
.         
. // This is a more restricted definition of swithchin
. gen switching_estrict = . 
{txt}(64,814 missing values generated)
{com}. replace switching_estrict = 1 if vot12_14==1 & pres16==2 // Democrat to Rep
{txt}(207 real changes made)
{com}. replace switching_estrict = 0 if vot12_14==1 & pres16==1 // Democrat to Democrat
{txt}(283 real changes made)
{com}.     
. 
. {c )-}    
. {c )-}
. *############################################
. * Saving the data
. *############################################
. {c -(}
. * Labels
. {c -(}
. lab var  switching2_broad "Switching"
. lab var  switching_estrict "Switching"
. lab var rti "RTI"
. lab var female "Female"
. lab var age "Age"
. lab var educ "Education"
. lab var black "Black"
. lab var unemployed "Unemployed"
. lab var foreign "Foreign born"
. lab var nonrelig "Non-Believer"
. lab var offshwalt2 "Offshorability"
. lab var offshwalt2 "Offshorability"
. lab var relskillspec "Skill-Specificity"
. lab var t2 "Task-Tech"
. lab var t3 "Task-Inter"
. lab var task3cog1 "Non-Routine"
. lab var task3cog2and3 "Routine"
. 
. 
.         
. {c )-}       
. 
. 
. keep id switching2_broad   rti age  female   foreign black unemployed nonrelig rincome  region  offshwalt2 relskillspec t2 t3 rincome wtssnr year educ switching_estrict task3cog2and3
. 
. save "Data\GSS.dta", replace
{txt}{p 0 4 2}
file {bf}
Data\GSS.dta{rm}
saved
{p_end}
{com}. 
. {c )-}
. {c )-}
{txt}
{com}. *##########################################
. * Alternatively load prepared data
. *##########################################
. {c -(}
. use "Data\GSS.dta", clear       
. {c )-}       
{txt}
{com}. *##########################################             
. * Analysis              
. *##########################################             
. {c -(}               
. {c -(}
. // Figure 3: The effect of exposure to automation on vote-switching. [US Part]          
. {c -(}               
. // Graph style          
. grstyle clear           
. set scheme s2color              
. grstyle init            
{res}{com}. grstyle set plain, nogrid               
. grstyle color background white          
.                 
.                 
. logit  switching_estrict  rti female age  foreign   rincome offshwalt2  relskillspec t2 t3 i.region  [pw=wtssnr] if year==2018  

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-167.30402}  
Iteration 1:{space 3}log pseudolikelihood = {res:-130.42312}  
Iteration 2:{space 3}log pseudolikelihood = {res:-129.95109}  
Iteration 3:{space 3}log pseudolikelihood = {res:-129.94784}  
Iteration 4:{space 3}log pseudolikelihood = {res:-129.94784}  
{res}
{txt}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:243}
{txt}{col 57}{lalign 13:Wald chi2({res:17})}{col 70} = {res}{ralign 6:64.26}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}Log pseudolikelihood = {res:-129.94784}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.2233}

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}switching_estrict{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      z{col 51}   P>|z|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}rti {c |}{col 19}{res}{space 2} .5658073{col 31}{space 2} .2273346{col 42}{space 1}    2.49{col 51}{space 3}0.013{col 59}{space 4} .1202397{col 72}{space 3} 1.011375
{txt}{space 11}female {c |}{col 19}{res}{space 2}-1.128794{col 31}{space 2} .4069663{col 42}{space 1}   -2.77{col 51}{space 3}0.006{col 59}{space 4}-1.926433{col 72}{space 3}-.3311546
{txt}{space 14}age {c |}{col 19}{res}{space 2} .0301868{col 31}{space 2}  .011984{col 42}{space 1}    2.52{col 51}{space 3}0.012{col 59}{space 4} .0066985{col 72}{space 3} .0536751
{txt}{space 10}foreign {c |}{col 19}{res}{space 2}-1.895753{col 31}{space 2} .5648859{col 42}{space 1}   -3.36{col 51}{space 3}0.001{col 59}{space 4}-3.002909{col 72}{space 3}-.7885968
{txt}{space 10}rincome {c |}{col 19}{res}{space 2} .0511515{col 31}{space 2} .0690097{col 42}{space 1}    0.74{col 51}{space 3}0.459{col 59}{space 4} -.084105{col 72}{space 3} .1864079
{txt}{space 7}offshwalt2 {c |}{col 19}{res}{space 2}-.8056715{col 31}{space 2} .4105859{col 42}{space 1}   -1.96{col 51}{space 3}0.050{col 59}{space 4}-1.610405{col 72}{space 3}-.0009378
{txt}{space 5}relskillspec {c |}{col 19}{res}{space 2}-.0305749{col 31}{space 2}  .040406{col 42}{space 1}   -0.76{col 51}{space 3}0.449{col 59}{space 4}-.1097693{col 72}{space 3} .0486194
{txt}{space 15}t2 {c |}{col 19}{res}{space 2}-.0996389{col 31}{space 2} .4884826{col 42}{space 1}   -0.20{col 51}{space 3}0.838{col 59}{space 4}-1.057047{col 72}{space 3} .8577693
{txt}{space 15}t3 {c |}{col 19}{res}{space 2} -.079217{col 31}{space 2} .5310481{col 42}{space 1}   -0.15{col 51}{space 3}0.881{col 59}{space 4}-1.120052{col 72}{space 3} .9616181
{txt}{space 17} {c |}
{space 11}region {c |}
{space 1}middle atlantic  {c |}{col 19}{res}{space 2}-.5430042{col 31}{space 2}  .810176{col 42}{space 1}   -0.67{col 51}{space 3}0.503{col 59}{space 4} -2.13092{col 72}{space 3} 1.044912
{txt}{space 1}e. nor. central  {c |}{col 19}{res}{space 2}-.1454866{col 31}{space 2}  .778773{col 42}{space 1}   -0.19{col 51}{space 3}0.852{col 59}{space 4}-1.671854{col 72}{space 3}  1.38088
{txt}{space 1}w. nor. central  {c |}{col 19}{res}{space 2} 1.484793{col 31}{space 2}  .908646{col 42}{space 1}    1.63{col 51}{space 3}0.102{col 59}{space 4}-.2961209{col 72}{space 3} 3.265706
{txt}{space 2}south atlantic  {c |}{col 19}{res}{space 2} .3657923{col 31}{space 2} .7929829{col 42}{space 1}    0.46{col 51}{space 3}0.645{col 59}{space 4}-1.188426{col 72}{space 3}  1.92001
{txt}{space 1}e. sou. central  {c |}{col 19}{res}{space 2}-.4843724{col 31}{space 2} .9668615{col 42}{space 1}   -0.50{col 51}{space 3}0.616{col 59}{space 4}-2.379386{col 72}{space 3} 1.410641
{txt}{space 1}w. sou. central  {c |}{col 19}{res}{space 2}  .694538{col 31}{space 2}    .8023{col 42}{space 1}    0.87{col 51}{space 3}0.387{col 59}{space 4}-.8779411{col 72}{space 3} 2.267017
{txt}{space 8}mountain  {c |}{col 19}{res}{space 2} 1.481022{col 31}{space 2}  1.04107{col 42}{space 1}    1.42{col 51}{space 3}0.155{col 59}{space 4} -.559437{col 72}{space 3} 3.521482
{txt}{space 9}pacific  {c |}{col 19}{res}{space 2}-1.354386{col 31}{space 2} .9018321{col 42}{space 1}   -1.50{col 51}{space 3}0.133{col 59}{space 4}-3.121945{col 72}{space 3} .4131719
{txt}{space 17} {c |}
{space 12}_cons {c |}{col 19}{res}{space 2}-.9208033{col 31}{space 2} 1.288692{col 42}{space 1}   -0.71{col 51}{space 3}0.475{col 59}{space 4}-3.446593{col 72}{space 3} 1.604986
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}.                 
. margins, atmeans at(rti=(-1.52(0.1)2.24))               
{res}
{txt}Adjusted predictions{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:243}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(switching_estrict), predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:-1.52}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:2._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:-1.42}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:3._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:-1.32}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:4._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:-1.22}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:5._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:-1.12}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:6._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:-1.02}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:7._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:-.92}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:8._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:-.82}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:9._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:-.72}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:10._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:-.62}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:11._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:-.52}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:12._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:-.42}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:13._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:-.32}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:14._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:-.22}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:15._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:-.12}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:16._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:-.02}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:17._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:.08}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:18._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:.18}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:19._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:.28}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:20._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:.38}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:21._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:.48}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:22._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:.58}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:23._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:.68}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:24._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:.78}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:25._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:.88}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:26._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:.98}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:27._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:1.08}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:28._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:1.18}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:29._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:1.28}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:30._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:1.38}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:31._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:1.48}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:32._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:1.58}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:33._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:1.68}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:34._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:1.78}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:35._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:1.88}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:36._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:1.98}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:37._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:2.08}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}
{lalign 8:38._at: }{space 0}{lalign 12:rti} = {res:{ralign 8:2.18}}
{lalign 8:}{space 0}{lalign 12:female} = {res:{ralign 8:.532048}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:age} = {res:{ralign 8:47.7697}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:foreign} = {res:{ralign 8:.0820038}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:rincome} = {res:{ralign 8:10.60962}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:offshwalt2} = {res:{ralign 8:.4581331}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:relskillspec} = {res:{ralign 8:4.348051}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t2} = {res:{ralign 8:.2862899}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:t3} = {res:{ralign 8:.3541626}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:1.region} = {res:{ralign 8:.0445602}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:2.region} = {res:{ralign 8:.1714176}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:3.region} = {res:{ralign 8:.1504749}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:4.region} = {res:{ralign 8:.0813046}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:5.region} = {res:{ralign 8:.2221665}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:6.region} = {res:{ralign 8:.0361411}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:7.region} = {res:{ralign 8:.0841592}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:8.region} = {res:{ralign 8:.0927575}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 12:9.region} = {res:{ralign 8:.1170185}} {txt:(mean)}

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{txt}{space 9}37  {c |}{col 14}{res}{space 2} .7374927{col 26}{space 2}  .102024{col 37}{space 1}    7.23{col 46}{space 3}0.000{col 54}{space 4} .5375293{col 67}{space 3} .9374562
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{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}.      marginsplot , recast(line) recastci(rline) ci1opts(fintensity(50) lpattern(dot)) xti(Risk of automation (RTI - Index)) yti(Predicted Probability of Switching (95% CI)) ti("US") saving("Figure\US.gph", replace)          
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:rti}{p_end}
{res}{txt}file {bf:Figure\US.gph} saved
{com}. {c )-}       
. lab var  switching2_broad "Switching broad" // broad
. lab var  switching_estrict "Switching strict"           // strict                       
. // table A3: Switching Vote, IV - RTI, US
. {c -(}
. eststo clear
. eststo: qui logit switching2_broad rti  female age   foreign educ i.rincome  [pw=wtssnr] if year==2018, robust                          
{txt}({res}est1{txt} stored)
{com}. eststo: qui logit switching2_broad rti  female age   foreign educ i.rincome offshwalt2  i.region [pw=wtssnr] if year==2018, robust      
{txt}({res}est2{txt} stored)
{com}. eststo: qui logit switching2_broad rti  female age   foreign educ i.rincome offshwalt2 relskillspec  i.region [pw=wtssnr] if year==2018, robust                 
{txt}({res}est3{txt} stored)
{com}. eststo: qui logit switching2_broad rti  female age   foreign educ i.rincome offshwalt2 relskillspec t2 t3 unemployed black nonrelig i.region [pw=wtssnr] if year==2018, robust          
{txt}({res}est4{txt} stored)
{com}. 
. esttab , replace label se title(Switching Vote, IV - RTI, US \label {c -(}tab:rtilong{c )-})mti("+Demographic" "+Offshoring" "+Skill" "All") compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(rti  female age   foreign educ offshwalt2 relskillspec t2 t3  unemployed black nonrelig) scalars( "N Observations" "r2_p R$^2 p$" "aic AIC" ) indicate( "Income = *.rincome"  "Regional controls = *.region")
{res}
{txt}Switching Vote, IV - RTI, US \label {tab:rtilong}
{txt}{hline 68}
{txt}                       (1)          (2)          (3)          (4)   
{txt}                 +Demogr~c    +Offsho~g       +Skill          All   
{txt}{hline 68}
{res}Switching broad                                                     {txt}
{txt}RTI             {res}     0.243*       0.376**      0.371**      0.396** {txt}
                {res} {ralign 9:{txt:(}0.125{txt:)}}    {ralign 9:{txt:(}0.146{txt:)}}    {ralign 9:{txt:(}0.146{txt:)}}    {ralign 9:{txt:(}0.171{txt:)}}   {txt}
{txt}Female          {res}    -0.453       -0.475       -0.448       -0.632** {txt}
                {res} {ralign 9:{txt:(}0.292{txt:)}}    {ralign 9:{txt:(}0.292{txt:)}}    {ralign 9:{txt:(}0.293{txt:)}}    {ralign 9:{txt:(}0.299{txt:)}}   {txt}
{txt}Age             {res}     0.040***     0.046***     0.046***     0.042***{txt}
                {res} {ralign 9:{txt:(}0.009{txt:)}}    {ralign 9:{txt:(}0.009{txt:)}}    {ralign 9:{txt:(}0.009{txt:)}}    {ralign 9:{txt:(}0.009{txt:)}}   {txt}
{txt}Foreign born    {res}    -1.982***    -1.898***    -1.873***    -1.925***{txt}
                {res} {ralign 9:{txt:(}0.564{txt:)}}    {ralign 9:{txt:(}0.581{txt:)}}    {ralign 9:{txt:(}0.578{txt:)}}    {ralign 9:{txt:(}0.657{txt:)}}   {txt}
{txt}Education       {res}    -0.047       -0.026       -0.025       -0.045   {txt}
                {res} {ralign 9:{txt:(}0.054{txt:)}}    {ralign 9:{txt:(}0.059{txt:)}}    {ralign 9:{txt:(}0.059{txt:)}}    {ralign 9:{txt:(}0.061{txt:)}}   {txt}
{txt}Offshorability  {res}                 -0.586**     -0.588**     -0.638*  {txt}
                {res}              {ralign 9:{txt:(}0.295{txt:)}}    {ralign 9:{txt:(}0.295{txt:)}}    {ralign 9:{txt:(}0.341{txt:)}}   {txt}
{txt}Skill-Specific~y{res}                               0.023        0.039   {txt}
                {res}                           {ralign 9:{txt:(}0.031{txt:)}}    {ralign 9:{txt:(}0.035{txt:)}}   {txt}
{txt}Task-Tech       {res}                                           -0.220   {txt}
                {res}                                        {ralign 9:{txt:(}0.410{txt:)}}   {txt}
{txt}Task-Inter      {res}                                            0.105   {txt}
                {res}                                        {ralign 9:{txt:(}0.427{txt:)}}   {txt}
{txt}Unemployed      {res}                                           -0.865   {txt}
                {res}                                        {ralign 9:{txt:(}0.900{txt:)}}   {txt}
{txt}Black           {res}                                           -3.767***{txt}
                {res}                                        {ralign 9:{txt:(}1.070{txt:)}}   {txt}
{txt}Non-Believer    {res}                                           -2.021***{txt}
                {res}                                        {ralign 9:{txt:(}0.510{txt:)}}   {txt}
{txt}Income          {res}       Yes          Yes          Yes          Yes   {txt}
{txt}Regional contr~s{res}        No          Yes          Yes          Yes   {txt}
{txt}{hline 68}
{txt}Observations    {res}       704          704          704          704   {txt}
{txt}R$^2 p$         {res}     0.100        0.158        0.159        0.268   {txt}
{txt}AIC             {res}   583.652      565.677      567.117      509.700   {txt}
{txt}{hline 68}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}. 
. esttab using "Table\USlong_2.tex", replace label se title(Switching Vote, IV - RTI, US \label {c -(}tab:rtilong2{c )-})mti("+Demographic" "+Offshoring" "+Skill" "All") compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(rti  female age   foreign educ offshwalt2 relskillspec t2 t3  unemployed black nonrelig) scalars( "N Observations" "r2_p R$^2 p$" "aic AIC" ) indicate( "Income = *.rincome"  "Regional controls = *.region")
{res}{txt}(output written to {browse  `"Table\USlong_2.tex"'})
{com}.         
. {c )-}       
. // table A5: Switching Vote (alternative definition), IV - RTI          
. {c -(}
. eststo clear
. eststo: qui logit switching_estrict rti  female age   foreign educ i.rincome  i.region [pw=wtssnr] if year==2018, robust                
{txt}({res}est1{txt} stored)
{com}. eststo: qui logit switching_estrict rti  female age   foreign educ i.rincome offshwalt2  i.region [pw=wtssnr] if year==2018, robust             
{txt}({res}est2{txt} stored)
{com}. eststo: qui logit switching_estrict rti  female age   foreign educ i.rincome offshwalt2 relskillspec  i.region [pw=wtssnr] if year==2018, robust                
{txt}({res}est3{txt} stored)
{com}. eststo: qui logit switching_estrict rti  female age   foreign educ i.rincome offshwalt2 relskillspec t2 t3 unemployed black nonrelig i.region [pw=wtssnr] if year==2018, robust         
{txt}({res}est4{txt} stored)
{com}. 
. esttab , replace label se title(Switching Vote (alternative definition), IV - RTI, US \label {c -(}tab:rtilongstrict{c )-})mti("+Demographic" "+Offshoring" "+Skill" "All") compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(rti  female age   foreign educ offshwalt2 relskillspec t2 t3  unemployed black nonrelig) scalars( "N Observations" "r2_p R$^2 p$" "aic AIC" )  indicate( "Income = *.rincome"  "Regional controls = *.region")              
{res}
{txt}Switching Vote (alternative definition), IV - RTI, US \label {tab:rtilongstrict}
{txt}{hline 68}
{txt}                       (1)          (2)          (3)          (4)   
{txt}                 +Demogr~c    +Offsho~g       +Skill          All   
{txt}{hline 68}
{res}Switching strict                                                    {txt}
{txt}RTI             {res}     0.339*       0.388*       0.419*       0.821***{txt}
                {res} {ralign 9:{txt:(}0.198{txt:)}}    {ralign 9:{txt:(}0.211{txt:)}}    {ralign 9:{txt:(}0.218{txt:)}}    {ralign 9:{txt:(}0.259{txt:)}}   {txt}
{txt}Female          {res}    -0.910**     -0.961**     -1.027***    -1.111** {txt}
                {res} {ralign 9:{txt:(}0.375{txt:)}}    {ralign 9:{txt:(}0.381{txt:)}}    {ralign 9:{txt:(}0.394{txt:)}}    {ralign 9:{txt:(}0.486{txt:)}}   {txt}
{txt}Age             {res}     0.038***     0.042***     0.042***     0.025   {txt}
                {res} {ralign 9:{txt:(}0.013{txt:)}}    {ralign 9:{txt:(}0.013{txt:)}}    {ralign 9:{txt:(}0.013{txt:)}}    {ralign 9:{txt:(}0.016{txt:)}}   {txt}
{txt}Foreign born    {res}    -2.445***    -2.377***    -2.421***    -2.132** {txt}
                {res} {ralign 9:{txt:(}0.654{txt:)}}    {ralign 9:{txt:(}0.701{txt:)}}    {ralign 9:{txt:(}0.704{txt:)}}    {ralign 9:{txt:(}0.900{txt:)}}   {txt}
{txt}Education       {res}    -0.259***    -0.241***    -0.242***    -0.233***{txt}
                {res} {ralign 9:{txt:(}0.074{txt:)}}    {ralign 9:{txt:(}0.078{txt:)}}    {ralign 9:{txt:(}0.079{txt:)}}    {ralign 9:{txt:(}0.081{txt:)}}   {txt}
{txt}Offshorability  {res}                 -0.559       -0.568       -1.154** {txt}
                {res}              {ralign 9:{txt:(}0.396{txt:)}}    {ralign 9:{txt:(}0.397{txt:)}}    {ralign 9:{txt:(}0.578{txt:)}}   {txt}
{txt}Skill-Specific~y{res}                              -0.037       -0.050   {txt}
                {res}                           {ralign 9:{txt:(}0.038{txt:)}}    {ralign 9:{txt:(}0.045{txt:)}}   {txt}
{txt}Task-Tech       {res}                                            0.007   {txt}
                {res}                                        {ralign 9:{txt:(}0.642{txt:)}}   {txt}
{txt}Task-Inter      {res}                                           -0.407   {txt}
                {res}                                        {ralign 9:{txt:(}0.698{txt:)}}   {txt}
{txt}Unemployed      {res}                                           -0.575   {txt}
                {res}                                        {ralign 9:{txt:(}0.909{txt:)}}   {txt}
{txt}Black           {res}                                           -5.275***{txt}
                {res}                                        {ralign 9:{txt:(}1.404{txt:)}}   {txt}
{txt}Non-Believer    {res}                                           -2.122***{txt}
                {res}                                        {ralign 9:{txt:(}0.590{txt:)}}   {txt}
{txt}Income          {res}       Yes          Yes          Yes          Yes   {txt}
{txt}Regional contr~s{res}       Yes          Yes          Yes          Yes   {txt}
{txt}{hline 68}
{txt}Observations    {res}       236          236          236          236   {txt}
{txt}R$^2 p$         {res}     0.264        0.272        0.274        0.475   {txt}
{txt}AIC             {res}   283.080      282.530      283.818      228.620   {txt}
{txt}{hline 68}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}. 
. esttab using "Table\USStrict_2.tex", replace label se title(Switching Vote (alternative definition), IV - RTI, US \label {c -(}tab:rtilongstrict{c )-})mti("+Demographic" "+Offshoring" "+Skill" "All") compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(rti  female age   foreign educ offshwalt2 relskillspec t2 t3  unemployed black nonrelig) scalars( "N Observations" "r2_p R$^2 p$" "aic AIC" )  indicate( "Income = *.rincome"  "Regional controls = *.region")          
{res}{txt}(output written to {browse  `"Table\USStrict_2.tex"'})
{com}.         
. {c )-}               
. // All table summarized. 
. {c -(}
. // table A6: Switching Vote, IV - Routine (dummy), US           
. eststo clear
. // Routine Dummy
. eststo: qui logit switching2_broad task3cog2and3  female age   foreign educ i.rincome  i.region [pw=wtssnr] if year==2018, robust               
{txt}({res}est1{txt} stored)
{com}. eststo: qui logit switching2_broad task3cog2and3  female age   foreign educ i.rincome offshwalt2  i.region [pw=wtssnr] if year==2018, robust
{txt}({res}est2{txt} stored)
{com}. eststo: qui logit switching2_broad task3cog2and3  female age   foreign educ i.rincome offshwalt2 relskillspec i.region [pw=wtssnr] if year==2018, robust                
{txt}({res}est3{txt} stored)
{com}.                 
. eststo: qui logit switching2_broad task3cog2and3  female age   foreign educ i.rincome offshwalt2 relskillspec t2 t3 unemployed black nonrelig i.region [pw=wtssnr] if year==2018, robust                
{txt}({res}est4{txt} stored)
{com}. 
. esttab , replace label se title(Switching Vote, IV - Routine (dummy), US \label {c -(}tab:task3cog2and3{c )-})mti("+Demographic" "+Offshoring" "+Skill" "All") compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(task3cog2and3  female age   foreign educ offshwalt2 relskillspec t2 t3  unemployed black nonrelig) scalars( "N Observations" "r2_p R$^2 p$" "aic AIC" ) indicate( "Income = *.rincome"  "Regional controls = *.region")          
{res}
{txt}Switching Vote, IV - Routine (dummy), US \label {tab:task3cog2and3}
{txt}{hline 68}
{txt}                       (1)          (2)          (3)          (4)   
{txt}                 +Demogr~c    +Offsho~g       +Skill          All   
{txt}{hline 68}
{res}Switching broad                                                     {txt}
{txt}Routine         {res}     0.950***     0.900***     0.865***     0.873***{txt}
                {res} {ralign 9:{txt:(}0.308{txt:)}}    {ralign 9:{txt:(}0.306{txt:)}}    {ralign 9:{txt:(}0.310{txt:)}}    {ralign 9:{txt:(}0.329{txt:)}}   {txt}
{txt}Female          {res}    -0.364       -0.373       -0.359       -0.577** {txt}
                {res} {ralign 9:{txt:(}0.273{txt:)}}    {ralign 9:{txt:(}0.275{txt:)}}    {ralign 9:{txt:(}0.276{txt:)}}    {ralign 9:{txt:(}0.291{txt:)}}   {txt}
{txt}Age             {res}     0.045***     0.046***     0.048***     0.042***{txt}
                {res} {ralign 9:{txt:(}0.009{txt:)}}    {ralign 9:{txt:(}0.008{txt:)}}    {ralign 9:{txt:(}0.009{txt:)}}    {ralign 9:{txt:(}0.009{txt:)}}   {txt}
{txt}Foreign born    {res}    -1.592***    -1.562***    -1.528***    -1.590***{txt}
                {res} {ralign 9:{txt:(}0.508{txt:)}}    {ralign 9:{txt:(}0.503{txt:)}}    {ralign 9:{txt:(}0.505{txt:)}}    {ralign 9:{txt:(}0.559{txt:)}}   {txt}
{txt}Education       {res}     0.021        0.022        0.020        0.013   {txt}
                {res} {ralign 9:{txt:(}0.055{txt:)}}    {ralign 9:{txt:(}0.055{txt:)}}    {ralign 9:{txt:(}0.055{txt:)}}    {ralign 9:{txt:(}0.058{txt:)}}   {txt}
{txt}Offshorability  {res}                 -0.305       -0.326       -0.516   {txt}
                {res}              {ralign 9:{txt:(}0.261{txt:)}}    {ralign 9:{txt:(}0.269{txt:)}}    {ralign 9:{txt:(}0.335{txt:)}}   {txt}
{txt}Skill-Specific~y{res}                               0.016        0.044   {txt}
                {res}                           {ralign 9:{txt:(}0.033{txt:)}}    {ralign 9:{txt:(}0.036{txt:)}}   {txt}
{txt}Task-Tech       {res}                                           -0.482   {txt}
                {res}                                        {ralign 9:{txt:(}0.403{txt:)}}   {txt}
{txt}Task-Inter      {res}                                           -0.299   {txt}
                {res}                                        {ralign 9:{txt:(}0.396{txt:)}}   {txt}
{txt}Unemployed      {res}                                           -1.111   {txt}
                {res}                                        {ralign 9:{txt:(}0.956{txt:)}}   {txt}
{txt}Black           {res}                                           -3.861***{txt}
                {res}                                        {ralign 9:{txt:(}1.077{txt:)}}   {txt}
{txt}Non-Believer    {res}                                           -2.095***{txt}
                {res}                                        {ralign 9:{txt:(}0.509{txt:)}}   {txt}
{txt}Income          {res}       Yes          Yes          Yes          Yes   {txt}
{txt}Regional contr~s{res}       Yes          Yes          Yes          Yes   {txt}
{txt}{hline 68}
{txt}Observations    {res}       770          770          767          767   {txt}
{txt}R$^2 p$         {res}     0.150        0.153        0.155        0.264   {txt}
{txt}AIC             {res}   613.107      613.307      610.766      547.849   {txt}
{txt}{hline 68}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}. 
. esttab using "Table\USdummy_2.tex", replace label se title(Switching Vote, IV - Routine (dummy), US \label {c -(}tab:task3cog2and3{c )-})mti("+Demographic" "+Offshoring" "+Skill" "All") compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(task3cog2and3  female age   foreign educ offshwalt2 relskillspec t2 t3  unemployed black nonrelig) scalars( "N Observations" "r2_p R$^2 p$" "aic AIC" ) indicate( "Income = *.rincome"  "Regional controls = *.region")               
{res}{txt}(output written to {browse  `"Table\USdummy_2.tex"'})
{com}. 
. 
. 
. 
.         
. {c )-}
.         
. {c )-}
. {c )-}               
{txt}
{com}. *##########################################             
. * Descriptive           
. *##########################################             
. {c -(}               
.                 
. // table A1: Descriptive statistic: USA GSS 2016 vs 2012                
. {c -(}               
.         eststo clear
. 
. qui estpost sum switching2_broad   rti age  female   foreign black unemployed nonrelig rincome  region  offshwalt2 relskillspec t2 t3  [w=wtssnr] if year==2018, d              
. 
.         
. esttab ,  /// ,  ,              
>         cells("mean(label(Mean) fmt(2)) p50(label(Median) fmt(2)) sd(label(S.D.) fmt(2)) min(label(Min.) fmt(0)) max(label(Max) fmt(0)) count(label(Obs.) fmt(0))") /// 
>         nonumber label replace noobs varlabels(switching2_broad "Vote Switching" rti "RTI" age "Age" female "Female" foreign "Foreign born" black "Black" unemployed "Unemployed" nonrelig "Non-Believer" rincome "Income Level" offshwalt2 "Offshorability" relskillspec "Skill-Specificity" t2 "Task-Tech" t3 "Task-Inte") nomtitle   
{res}
{txt}{hline 98}
{txt}                             Mean       Median         S.D.         Min.          Max         Obs.
{txt}{hline 98}
{txt}Vote Switching      {res}         0.15         0.00         0.36            0            1         1468{txt}
{txt}RTI                 {res}        -0.11        -0.44         0.98           -2            2         2045{txt}
{txt}Age                 {res}        46.67        45.00        17.74           18           89         2341{txt}
{txt}Female              {res}         0.54         1.00         0.50            0            1         2348{txt}
{txt}Foreign born        {res}         0.14         0.00         0.34            0            1         2347{txt}
{txt}Black               {res}         0.15         0.00         0.36            0            1         2348{txt}
{txt}Unemployed          {res}         0.04         0.00         0.19            0            1         2348{txt}
{txt}Non-Believer        {res}         0.24         0.00         0.43            0            1         2348{txt}
{txt}Income Level        {res}        10.30        12.00         3.06            1           12         1315{txt}
{txt}region of interview {res}         5.19         5.00         2.51            1            9         2348{txt}
{txt}Offshorability      {res}         0.42         0.00         0.49            0            1         2248{txt}
{txt}Skill-Specificity   {res}         4.12         3.38         3.32            1           25         2243{txt}
{txt}Task-Tech           {res}         0.27         0.00         0.44            0            1         2248{txt}
{txt}Task-Inte           {res}         0.38         0.00         0.49            0            1         2248{txt}
{txt}{hline 98}
{com}.                 
. esttab using  "Table\summarystats_US.tex",  /// ,  ,            
>         cells("mean(label(Mean) fmt(2)) p50(label(Median) fmt(2)) sd(label(S.D.) fmt(2)) min(label(Min.) fmt(0)) max(label(Max) fmt(0)) count(label(Obs.) fmt(0))") /// 
>         nonumber label replace noobs varlabels(switching2_broad "Vote Switching" rti "RTI" age "Age" female "Female" foreign "Foreign born" black "Black" unemployed "Unemployed" nonrelig "Non-Believer" rincome "Income Level" offshwalt2 "Offshorability" relskillspec "Skill-Specificity" t2 "Task-Tech" t3 "Task-Inte") nomtitle   
{res}{txt}(output written to {browse  `"Table\summarystats_US.tex"'})
{com}. {c )-}               
. {c )-}               
{txt}
{com}. 
{txt}end of do-file

{com}. do "./Do/1_2_Switching_Germany.do"
{txt}
{com}. *****************************************************************************
. *               Cleaning and Analyzing - Switching vote Germany                  *
. *                                                                                                                                           *
. * Author:                       Valentina Gonzalez-Rostani                                                      *
. * Contact:                      mag384@pitt.edu                                                                 *
. * Date:                         March 21 2022                                                                   *
. * Version:                      Stata 17                                                                        *
. *                                                                                                                                                       *
. *****************************************************************************
. /*
> This do-file:
>         * Processing of the data
>                 * Call the Data
>                 * Define variables
>                 * Save the data
>         * Load preapred data **line 869**
>         * Analysis and Descriptives: Export Tables &  Figure
> 
> Input: GSS data
>         - Data\Switching\pgen.dta
>         - Figure/US.gph // this will be used to merge with German.gph (i.e you should run first 1_1_Switching_US.do)
> 
> Final output:
>         Cleaned data: 
>                 * "Data\SOEP.dta" this data contains the relevant variables for the analysis.
>         Tables:
>                 * table A4: Switching Vote (Only left) - Germany, IV - RTI [Table\SDU.tex]
>                 * table A7: Switching Vote From Establishment Left and Right to Populist Right, IV - RTI, German [Table\SOEPlong_2.tex]
>                 * table A8: Switching Vote, IV - Routine (dummy), Germany [Table\SOEPdummy_2.tex]
>                 * table A9: Switching Vote (Only from the Right), IV - RTI [Table\CDU.tex]
>         Figure:
>             * Figure 3: The effect of exposure to automation on vote-switching. [German Part]  [Figure/Germany.gph]
>                 * Figure 3: The effect of exposure to automation on vote-switching. [Merging gph US and German part] [Figure/Graph_US_Germany.pdf]
> 
> 
> */
. ** Set directory
. cd "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication"
{res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication
{txt}
{com}. 
. *##########################################
. * Processing of the data (alternatively skip and go to line 869)
. *##########################################
. {c -(}
. ************************************************************************
. **************** A. Calling the data  **********************************
. ************************************************************************
. {c -(}
. 
. * Calling the data
. use "Data\not_for_dataverse\pgen.dta", clear
. 
. 
. keep if syear==2014 | syear==2018 // since elections 2013 vs 2017
{txt}(236,044 observations deleted)
{com}. 
. 
. {c )-}
. 
. 
. ************************************************************************
. ********************* B. Demographic  **********************************
. ************************************************************************
. {c -(}
. * Dummy for born outside the US
. gen foreign=.
{txt}(56,109 missing values generated)
{com}. replace foreign=0 if pgnation==1
{txt}(46,212 real changes made)
{com}. replace foreign=. if pgnation<1
{txt}(0 real changes made)
{com}. replace foreign=1 if pgnation>1 & pgnation~=.
{txt}(9,897 real changes made)
{com}. 
. * Dummy for nonreligion
. gen nonrelig=1
. replace nonrelig=0 if plh0258_h>0 & plh0258_h<12
{txt}(464 real changes made)
{com}. replace nonrelig=. if plh0258_h<0
{txt}(54,439 real changes made, 54,439 to missing)
{com}. 
. gen educ=pgbilzeit
. replace educ=. if pgbilzeit<0
{txt}(4,200 real changes made, 4,200 to missing)
{com}. replace educ=. if pgbilzeit>40
{txt}(0 real changes made)
{com}. 
. * Dummy for high level of education
. gen high=(educ>13) // 13 as cut off because 13.5 is the 75% percentile
. 
. * Dummy for female
. gen female =. 
{txt}(56,109 missing values generated)
{com}. replace female = 1 if sex==2
{txt}(29,669 real changes made)
{com}. replace female = 0 if sex==1
{txt}(26,440 real changes made)
{com}. 
. * Calculating age
. destring syear gebjahr, replace
{txt}syear already numeric; no {res}replace
{txt}gebjahr already numeric; no {res}replace
{com}. gen age = . 
{txt}(56,109 missing values generated)
{com}. replace age = syear-gebjahr if gebjahr>0 & gebjahr~=. 
{txt}(56,109 real changes made)
{com}. replace age=. if syear<0 | syear==. 
{txt}(0 real changes made)
{com}. replace age=. if gebjahr<0
{txt}(0 real changes made)
{com}. 
. * Dummy for unemployed
. gen unemployed=. 
{txt}(56,109 missing values generated)
{com}. replace unemployed=1 if pglfs ==6
{txt}(3,760 real changes made)
{com}. replace unemployed=0 if pglfs ~=6 
{txt}(52,349 real changes made)
{com}. replace unemployed=. if pglfs <0
{txt}(28 real changes made, 28 to missing)
{com}. 
. gen income=. 
{txt}(56,109 missing values generated)
{com}. replace income=plb0471_h
{txt}(54,903 real changes made)
{com}. replace income=. if plb0471_h<0 | plb0471_h==. 
{txt}(27,311 real changes made, 27,311 to missing)
{com}. 
. 
. gen rincome = . 
{txt}(56,109 missing values generated)
{com}. replace rincome=1 if income<1000
{txt}(5,430 real changes made)
{com}. replace rincome=2 if income<2999 & income>999
{txt}(13,199 real changes made)
{com}. replace rincome=3 if income<3999 & income>3000
{txt}(3,493 real changes made)
{com}. replace rincome=4 if income<4999 & income>4000
{txt}(1,673 real changes made)
{com}. replace rincome=5 if income<5999 & income>5000
{txt}(870 real changes made)
{com}. replace rincome=6 if income<6999 & income>6000
{txt}(469 real changes made)
{com}. replace rincome=7 if income<7999 & income>7000
{txt}(233 real changes made)
{com}. replace rincome=8 if income<9999 & income>8000
{txt}(192 real changes made)
{com}. replace rincome=9 if income<15000 & income>10000
{txt}(105 real changes made)
{com}. replace rincome=10 if income<20000 & income>14999
{txt}(18 real changes made)
{com}. replace rincome=11 if income<25000 & income>19999
{txt}(13 real changes made)
{com}. replace rincome=12 if income>24999 & income~=.
{txt}(7 real changes made)
{com}. 
. {c )-}
. 
. 
. ************************************************************************
. ********************* C. Parties  **************************************
. ************************************************************************
. {c -(}
. * Populist [only Afd]
. gen populist=. 
{txt}(56,109 missing values generated)
{com}. replace populist = 1 if plh0012_h==27 //Alternative fur Deutschland (AfD)
{txt}(897 real changes made)
{com}. replace populist = 0 if plh0012_h==26 //Piratenpartei
{txt}(104 real changes made)
{com}. replace populist = 0 if plh0012_h==7 //NDP, Republicans, The Right
{txt}(121 real changes made)
{com}. replace populist = 0 if plh0012_h~=27 &  plh0012_h~=26 & plh0012_h~=7  
{txt}(54,987 real changes made)
{com}. replace populist = . if plh0012_h<1
{txt}(32,840 real changes made, 32,840 to missing)
{com}. 
. 
. 
. gen establishment_left2=. 
{txt}(56,109 missing values generated)
{com}. replace establishment_left2 = 1 if plh0012_h==1 //soc social democratic - spd
{txt}(6,014 real changes made)
{com}. replace establishment_left2 = 1 if plh0012_h==4 //[4] FDP       lib liberal
{txt}(804 real changes made)
{com}. replace establishment_left2 = 0 if plh0012_h~=1 & plh0012_h~=4 
{txt}(49,291 real changes made)
{com}. replace establishment_left2 = . if plh0012_h<1
{txt}(32,840 real changes made, 32,840 to missing)
{com}. 
. gen establishment_right=. 
{txt}(56,109 missing values generated)
{com}. replace establishment_right = 1 if plh0012_h==2 
{txt}(7,125 real changes made)
{com}. replace establishment_right = 1 if plh0012_h==3 
{txt}(1,361 real changes made)
{com}. replace establishment_right = 0 if plh0012_h~=2 & plh0012_h~=3  
{txt}(47,623 real changes made)
{com}. replace establishment_right = . if plh0012_h<1
{txt}(32,840 real changes made, 32,840 to missing)
{com}. 
. {c )-}
. 
. ************************************************************************
. ********************* D. Standarizing occupations **********************
. ************************************************************************
. {c -(}
. gen year=syear
. gen iscoco = pgisco88
. replace iscoco = . if pgisco88<0
{txt}(29,493 real changes made, 29,493 to missing)
{com}. replace iscoco = p_isco88 if pgisco88<0
{txt}(28,287 real changes made)
{com}. replace iscoco = . if iscoco<0
{txt}(28,287 real changes made, 28,287 to missing)
{com}. 
. foreach var of varlist iscoco  {c -(}
{txt}  2{com}. gen str_`var'=string(`var')
{txt}  3{com}. replace str_`var'="0100" if str_`var'=="100"
{txt}  4{com}. {c )-}
{txt}(91 real changes made)
{com}. sum iscoco if str_iscoco=="0100"

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}iscoco {c |}{res}         91         100           0        100        100
{com}. foreach x in iscoco {c -(}
{txt}  2{com}. gen `x'2 = substr(str_`x',1,2) if `x'<9334
{txt}  3{com}. replace `x'2 = "." if `x'2==""
{txt}  4{com}. {c )-}
{txt}(29,493 missing values generated)
(29,493 real changes made)
{com}. 
. drop str_*
. 
. destring iscoco2, replace
{txt}iscoco2: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(29493 missing values generated)
{res}{com}. 
. label var iscoco2 "ISCO88 at the 2-digit"
. 
. * Labels Iscoco
. {c -(}
. gen iscoco2n="."
. replace iscoco2n="Legislators and senior officials" if iscoco2==11
{txt}variable {bf}{res}iscoco2n{sf}{txt} was {bf}{res}str1{sf}{txt} now {bf}{res}str32{sf}
{txt}(53 real changes made)
{com}.         replace iscoco2n="Corporate managers" if iscoco2==12
{txt}(953 real changes made)
{com}.         replace iscoco2n="General managers" if iscoco2==13
{txt}(495 real changes made)
{com}.         replace iscoco2n="Physical, mathematical and engineering science professionals" if iscoco2==21
{txt}variable {bf}{res}iscoco2n{sf}{txt} was {bf}{res}str32{sf}{txt} now {bf}{res}str60{sf}
{txt}(1,553 real changes made)
{com}.         replace iscoco2n="Life science and health professionals" if iscoco2==22
{txt}(432 real changes made)
{com}.         replace iscoco2n="Teaching professionals" if iscoco2==23
{txt}(1,304 real changes made)
{com}.         replace iscoco2n="Other professionals" if iscoco2==24
{txt}(2,213 real changes made)
{com}.         replace iscoco2n="Physical and engineering science associate professionals" if iscoco2==31
{txt}(1,394 real changes made)
{com}.         replace iscoco2n="Life science and health associate professionals" if iscoco2==32
{txt}(1,055 real changes made)
{com}.         replace iscoco2n="Teaching associate professionals" if iscoco2==33
{txt}(812 real changes made)
{com}.         replace iscoco2n="Other associate professionals" if iscoco2==34
{txt}(3,295 real changes made)
{com}.         replace iscoco2n="Office clerks" if iscoco2==41
{txt}(2,175 real changes made)
{com}.         replace iscoco2n="Customer services clerks" if iscoco2==42
{txt}(413 real changes made)
{com}.         replace iscoco2n="Personal and protective services workers" if iscoco2==51
{txt}(2,258 real changes made)
{com}.         replace iscoco2n="Models, salespersons and demonstrators" if iscoco2==52
{txt}(1,041 real changes made)
{com}.         replace iscoco2n="Market-oriented skilled agricultural and fishery workers" if iscoco2==61
{txt}(316 real changes made)
{com}.         replace iscoco2n="Subsistence agricultural and fishery workers" if iscoco2==62
{txt}(0 real changes made)
{com}.         replace iscoco2n="Extraction and building trades workers" if iscoco2==71
{txt}(1,112 real changes made)
{com}.         replace iscoco2n="Metal, machinery and related trades workers" if iscoco2==72
{txt}(1,314 real changes made)
{com}.         replace iscoco2n="Precision, handicraft, printing and related trades workers" if iscoco2==73
{txt}(216 real changes made)
{com}.         replace iscoco2n="Other craft and related trades workers" if iscoco2==74
{txt}(395 real changes made)
{com}.         replace iscoco2n="Stationary-plant and related operators" if iscoco2==81
{txt}(201 real changes made)
{com}.         replace iscoco2n="Machine operators and assemblers" if iscoco2==82
{txt}(620 real changes made)
{com}.         replace iscoco2n="Drivers and mobile-plant operators" if iscoco2==83
{txt}(856 real changes made)
{com}.         replace iscoco2n="Sales and services elementary occupations" if iscoco2==91
{txt}(1,320 real changes made)
{com}.         replace iscoco2n="Agricultural, fishery and related labourers" if iscoco2==92
{txt}(69 real changes made)
{com}.         replace iscoco2n="Labourers in mining, construction, manufacturing and transport" if iscoco2==93
{txt}variable {bf}{res}iscoco2n{sf}{txt} was {bf}{res}str60{sf}{txt} now {bf}{res}str62{sf}
{txt}(660 real changes made)
{com}.         replace iscoco2n="Armed forces" if iscoco2==01
{txt}(91 real changes made)
{com}. label var iscoco2n "Names of iscoco2 coding"
. {c )-}
. ************************************************************************
. ********************* Exposure to automation ***************************
. ************************************************************************
. {c -(}
. ************************************************************************
. * 1. Goos et al.(2014)
. ************************************************************************
. {c -(}
. ** These lines include the routinisation and offshoring indices at the 2-digit level from Goos et al. (2014)
. 
. */ The idea is that for each occupation (at the 2-digit level) one risk is assigned. The weak part of this index is that it treats as the same the last 2-digit of the ISCO.
. * In other words, ISCO has 4 digit, but goos et al only consider the first 2 digit, therefore there is no differentiation among occupations that differ in the last 2 digit. 
. gen rti=.
{txt}(56,109 missing values generated)
{com}. 
. {c -(}
. replace rti=-0.75 if iscoco2==12
{txt}(953 real changes made)
{com}. replace rti=-0.82 if iscoco2==21
{txt}(1,553 real changes made)
{com}. replace rti=-1.00 if iscoco2==22
{txt}(432 real changes made)
{com}. replace rti=-0.73 if iscoco2==24
{txt}(2,213 real changes made)
{com}. replace rti=-1.52 if iscoco2==13
{txt}(495 real changes made)
{com}. replace rti=-0.40 if iscoco2==31
{txt}(1,394 real changes made)
{com}. replace rti=-0.44 if iscoco2==34
{txt}(3,295 real changes made)
{com}. replace rti=-0.33 if iscoco2==32
{txt}(1,055 real changes made)
{com}. 
. replace rti=0.32 if iscoco2==81
{txt}(201 real changes made)
{com}. replace rti=0.46 if iscoco2==72
{txt}(1,314 real changes made)
{com}. replace rti=-1.50 if iscoco2==83
{txt}(856 real changes made)
{com}. replace rti=2.24 if iscoco2==41
{txt}(2,175 real changes made)
{com}. replace rti=1.59 if iscoco2==73
{txt}(216 real changes made)
{com}. replace rti=-0.19 if iscoco2==71
{txt}(1,112 real changes made)
{com}. replace rti=1.41 if iscoco2==42
{txt}(413 real changes made)
{com}. replace rti=0.49 if iscoco2==82
{txt}(620 real changes made)
{com}. replace rti=1.24 if iscoco2==74
{txt}(395 real changes made)
{com}. 
. replace rti=0.45 if iscoco2==93
{txt}(660 real changes made)
{com}. replace rti=-0.60 if iscoco2==51
{txt}(2,258 real changes made)
{com}. replace rti=0.05 if iscoco2==52
{txt}(1,041 real changes made)
{com}. replace rti=0.03 if iscoco2==91
{txt}(1,320 real changes made)
{com}. {c )-}
. 
. label var rti "RTI index"
. 
. {c )-}
. ************************************************************************
. ***** 2. Oesch (2006) and Kitschelt and Rehm (2014) classification *****
. ************************************************************************
. {c -(}
. *Now is the same, but using Oesch (2006) and Kitschelt and Rehm (2014) classification
. 
. */  Oesch (2006) develops the bases of a new class schema that partly shifts its focus from hierarchical divisions to horizontal cleavages. The idea is  that the middle class is not conceptualized as a unitary grouping and the manual/non-manual divide is not used as a decisive class boundary. 
. */ The emphasis is put on differences in marketable skills and the work logic. 
. 
. // What is the logic? 
. */Logic of task structures: t1 organizational (taskorg), t2 technical (tasktech), and t3 interpersonal (taskinter)
. */Logic of authority: a1 professional (authprof), a2 associate professional (authassoc), a3 skilled routine (authskil) a4 unskilled routine (authunsk)
. */Leads to 3*4 groups, in regressions of K&R combined to 4 groups (skilled+unskilled routine all tasks; prof+assoc prof for 3 tasks separately)
. 
. * Code from Thewissen and Rueda  (2019)
. {c -(}
. */t1a1: Higher grade managers
. gen t1a1=1 if iscoco>=1000 & iscoco<=1251 | iscoco>=2410 & iscoco<=2419 | inlist(iscoco,2441,2470)
{txt}(54,023 missing values generated)
{com}. label var t1a1 "Higher grade managers"
. 
. */t1a2: Associate managers
. gen t1a2=1 if iscoco>=1252 & iscoco<=1319 | iscoco>=3410 & iscoco<=3449 | inlist(iscoco,3452)
{txt}(53,072 missing values generated)
{com}. label var t1a2 "Associate managers"
. 
. */t1a3: Skilled office
. gen t1a3=1 if iscoco>=4000 & iscoco<=4112 | iscoco>=4114 & iscoco<=4141 | inlist(iscoco,4143) | iscoco>=4190 & iscoco<=4210 | iscoco>=4213 & iscoco<=4221
{txt}(54,012 missing values generated)
{com}. label var t1a3 "Skilled office"
. 
. */t1a4: Routine office
. gen t1a4=1 if inlist(iscoco,4113,4142,4144) | iscoco>=4211 & iscoco<=4212 | iscoco>=4222 & iscoco<=4223
{txt}(55,618 missing values generated)
{com}. label var t1a4 "Routine office"
. 
. */t2a1: Technical experts
. gen t2a1=1 if iscoco>=2100 & iscoco<=2213
{txt}(54,510 missing values generated)
{com}. label var t2a1 "Technical experts"
. 
. */t2a2: Technicians
. gen t2a2=1 if iscoco>=3100 & iscoco<=3213 | inlist(iscoco,3471)
{txt}(54,539 missing values generated)
{com}. label var t2a2 "Technicians"
. 
. */t2a3: Skilled crafts
. gen t2a3=1 if inlist(iscoco,110,8311,8324,8333) | iscoco>=7120 & iscoco<=7142 | iscoco>=7200 & iscoco<=7233 | iscoco>=7240 & iscoco<=7423 | iscoco>=7430 & iscoco<=7520
{txt}(52,882 missing values generated)
{com}. label var t2a3 "Skilled crafts"
. 
. */t2a4: Routine operatives/agriculture
. gen t2a4=1 if inlist(iscoco,7143, 7234, 7424, 8312) | iscoco>=7100 & iscoco<=7113 | iscoco>=7129 & iscoco<=7130 | iscoco>=8000 & iscoco<=8310 | iscoco>=8334 & iscoco<=8400 | iscoco>=9160 & iscoco<=9162 | iscoco>=9300 & iscoco<=9333
{txt}(54,310 missing values generated)
{com}. replace t2a4=1 if iscoco>=6010 & iscoco<=6210 | iscoco>=8330 & iscoco<=8332 | iscoco>=9200 & iscoco<=9213
{txt}(483 real changes made)
{com}. label var t2a4 "Routine operatives/agriculture"
. 
. */t3a1: Socio-cultural professionals
. gen t3a1=1 if inlist(iscoco, 2359, 2445, 2451, 2460) | iscoco>=2220 & iscoco<=2323 | iscoco>=2350 & iscoco<=2351 |iscoco>=2420 & iscoco<=2440 | iscoco>=2442 & iscoco<=2443
{txt}(53,990 missing values generated)
{com}. label var t3a1 "Socio-cultural professionals"
. 
. */t3a2: Socio-cultural semi-professionals
. gen t3a2=1 if inlist(iscoco,2352, 2444, 3220, 3226) | iscoco>=2330 & iscoco<=2340 | iscoco>=2446 & iscoco<=2450 | iscoco>=2452 & iscoco<=2455 | iscoco>=3222 & iscoco<=3224 | iscoco>=3229 & iscoco<=3232 | iscoco>=3240 & iscoco<=3400 | iscoco>=3450 & iscoco<=3451 | iscoco>=3460 & iscoco<=3470 | iscoco>=3472 & iscoco<=3480
{txt}(53,068 missing values generated)
{com}. label var t3a2 "Socio-cultural semi-professionals"
. 
. */t3a3: Skilled service
. gen t3a3=1 if inlist(iscoco, 3221, 3225, 5122, 5141, 5143, 8323) | iscoco>=3227 & iscoco<=3228 | iscoco>=5110 & iscoco<=5113 | iscoco>=5150 & iscoco<=5163 | iscoco>=5200 & iscoco<=5210
{txt}(55,202 missing values generated)
{com}. label var t3a3 "Skilled service"
. 
. */t3a4: Routine service
. gen t3a4=1 if inlist(iscoco,5142, 5149, 5169) | iscoco>=5120 & iscoco<=5121 | iscoco>=5123 & iscoco<=5130 | iscoco>=5131 & iscoco<=5140 | iscoco>=5220 & iscoco<=5230 | iscoco>=8320 & iscoco<=8322 | iscoco>=9100 & iscoco<=9153
{txt}(52,023 missing values generated)
{com}. label var t3a4 "Routine service"
. 
. 
. forvalues i=1(1)3 {c -(}
{txt}  2{com}.         gen t`i'=1 if t`i'a1==1 | t`i'a2==1  | t`i'a3==1 | t`i'a4==1
{txt}  3{com}.         {c )-}
{txt}(48,398 missing values generated)
(47,472 missing values generated)
(45,956 missing values generated)
{com}. list t1-t3 if t1==t2 & t2==t3 & t1==t3 & t1~=.
. replace t1=0 if t2==1 | t3==1
{txt}(18,790 real changes made)
{com}. replace t2=0 if t1==1 | t3==1
{txt}(17,864 real changes made)
{com}. replace t3=0 if t1==1 | t2==1
{txt}(16,348 real changes made)
{com}. 
. label var t1 "Organisational task structure (t1a1, 2, 3, or 4==1)"
. label var t2 "Technical task structure (t2a1, 2, 3, or 4==1)"
. label var t3 "Interpersonal task structure (t3a1, 2, 3, or 4==1)"
. 
. forvalues i=1(1)4 {c -(}
{txt}  2{com}.         gen a`i'=1 if t1a`i'==1 | t2a`i'==1  | t3a`i'==1
{txt}  3{com}.         {c )-}
{txt}(50,305 missing values generated)
(48,461 missing values generated)
(49,878 missing values generated)
(49,250 missing values generated)
{com}. replace a1=0 if a2==1 | a3==1 | a4==1
{txt}(20,697 real changes made)
{com}. replace a2=0 if a1==1 | a3==1 | a4==1
{txt}(18,853 real changes made)
{com}. replace a3=0 if a1==1 | a2==1 | a4==1
{txt}(20,311 real changes made)
{com}. replace a4=0 if a1==1 | a2==1 | a3==1
{txt}(19,642 real changes made)
{com}. 
. label var a1 "Professional authority (t1a1, t2a1, or t3a1==1)"
. label var a2 "Assoc prof authority (t1a2, t2a2, or t3a2==1)"
. label var a3 "Skilled routine authority (t1a3, t2a3, or t3a3==1)"
. label var a4 "Unskilled routine authority (t1a4, t2a4, or t3a4==1)"
. 
. gen check = a1+a2+a3+a4
{txt}(29,608 missing values generated)
{com}. sum check

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}check {c |}{res}     26,501           1           0          1          1
{com}. drop check
. 
. gen c1=1 if t1a1==1 | t1a2==1
{txt}(50,986 missing values generated)
{com}. gen c2=1 if t2a1==1 | t2a2==1
{txt}(52,940 missing values generated)
{com}. gen c3=1 if t3a1==1 | t3a2==1
{txt}(50,949 missing values generated)
{com}. gen c4=1 if t1a3==1 | t1a4==1 | t2a3==1 | t2a4==1 | t3a3==1 | t3a4==1
{txt}(43,060 missing values generated)
{com}. 
. replace c1=0 if c2==1 | c3==1 | c4==1
{txt}(21,378 real changes made)
{com}. replace c2=0 if c1==1 | c3==1 | c4==1
{txt}(23,332 real changes made)
{com}. replace c3=0 if c1==1 | c2==1 | c4==1
{txt}(21,341 real changes made)
{com}. replace c4=0 if c1==1 | c2==1 | c3==1
{txt}(13,452 real changes made)
{com}. 
. label var c1 "Skilled organisational (t1a1 or t1a2==1)"
. label var c2 "Skilled technical (t2a1 or t2a2==1)"
. label var c3 "Skilled interpersonal (t3a1 or t3a2==1)"
. label var c4 "Unskilled routine (t1a3, t1a4, t2a3, t2a4, t3a3, or t3a4==1)"
. 
. gen check = c1+c2+c3+c4
{txt}(29,608 missing values generated)
{com}. sum check

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}check {c |}{res}     26,501           1           0          1          1
{com}. drop check
. {c )-}
. {c )-}
. *********************************************************************
. ***** 3. Offshoring index from Walter (2017), based on Blinder*******
. *********************************************************************
. {c -(}
. */ Stefanie Walter (2017) provides the code for offshorability based on Blinder. It uses ISCO with 4 digits. 
. 
. gen offshwalt=.
{txt}(56,109 missing values generated)
{com}. label var offshwalt "Offshoring Potential (Blinder) from Walter"
. 
. 
. *4-digit ISCO-code
.  * Coding is based on the classification developed in Blinder, Alan. 2007. "How Many U.S. Jobs Might Be Offshorable." CEPS Working Paper No. 142.
. * NOTE: all professions not listed by Blinder are coded as not offshorable (value 0)
. * Unlike Goos et al, Walter considers the 4 digits. 
. {c -(}
. replace offshwalt=0 if iscoco<.
{txt}(26,616 real changes made)
{com}. replace offshwalt=49 if iscoco==1142
{txt}(24 real changes made)
{com}. replace offshwalt=55 if iscoco==1222
{txt}(98 real changes made)
{com}. replace offshwalt=28 if iscoco==1226
{txt}(5 real changes made)
{com}. replace offshwalt=55 if iscoco==1228
{txt}(0 real changes made)
{com}. replace offshwalt=83 if iscoco==1231
{txt}(44 real changes made)
{com}. replace offshwalt=49 if iscoco==1232
{txt}(50 real changes made)
{com}. replace offshwalt=40 if iscoco==1233
{txt}(194 real changes made)
{com}. replace offshwalt=53 if iscoco==1234
{txt}(9 real changes made)
{com}. replace offshwalt=49 if iscoco==1235
{txt}(15 real changes made)
{com}. replace offshwalt=55 if iscoco==1236
{txt}(58 real changes made)
{com}. replace offshwalt=55 if iscoco==1237
{txt}(0 real changes made)
{com}. replace offshwalt=55 if iscoco==1311
{txt}(6 real changes made)
{com}. replace offshwalt=55 if iscoco==1312
{txt}(2 real changes made)
{com}. replace offshwalt=55 if iscoco==1313
{txt}(16 real changes made)
{com}. replace offshwalt=55 if iscoco==1314
{txt}(199 real changes made)
{com}. replace offshwalt=55 if iscoco==1315
{txt}(107 real changes made)
{com}. replace offshwalt=48 if iscoco==1316
{txt}(13 real changes made)
{com}. replace offshwalt=52 if iscoco==1317
{txt}(9 real changes made)
{com}. replace offshwalt=55 if iscoco==1318
{txt}(0 real changes made)
{com}. replace offshwalt=55 if iscoco==1319
{txt}(23 real changes made)
{com}. replace offshwalt=66 if iscoco==2111
{txt}(12 real changes made)
{com}. replace offshwalt=74 if iscoco==2112
{txt}(1 real change made)
{com}. replace offshwalt=66 if iscoco==2113
{txt}(25 real changes made)
{com}. replace offshwalt=66 if iscoco==2114
{txt}(6 real changes made)
{com}. replace offshwalt=89 if iscoco==2121
{txt}(5 real changes made)
{com}. replace offshwalt=96 if iscoco==2122
{txt}(4 real changes made)
{com}. replace offshwalt=83 if iscoco==2131
{txt}(183 real changes made)
{com}. replace offshwalt=90 if iscoco==2139
{txt}(225 real changes made)
{com}. replace offshwalt=25 if iscoco==2141
{txt}(120 real changes made)
{com}. replace offshwalt=64 if iscoco==2143
{txt}(119 real changes made)
{com}. replace offshwalt=74 if iscoco==2144
{txt}(90 real changes made)
{com}. replace offshwalt=72 if iscoco==2146
{txt}(49 real changes made)
{com}. replace offshwalt=69 if iscoco==2147
{txt}(7 real changes made)
{com}. replace offshwalt=86 if iscoco==2148
{txt}(9 real changes made)
{com}. replace offshwalt=71 if iscoco==2149
{txt}(257 real changes made)
{com}. replace offshwalt=81 if iscoco==2211
{txt}(18 real changes made)
{com}. replace offshwalt=83 if iscoco==2212
{txt}(2 real changes made)
{com}. replace offshwalt=72 if iscoco==2411
{txt}(86 real changes made)
{com}. replace offshwalt=50 if iscoco==2419
{txt}(442 real changes made)
{com}. replace offshwalt=74 if iscoco==2421
{txt}(149 real changes made)
{com}. replace offshwalt=67 if iscoco==2444
{txt}(32 real changes made)
{com}. replace offshwalt=90 if iscoco==2451
{txt}(152 real changes made)
{com}. replace offshwalt=78 if iscoco==2452
{txt}(24 real changes made)
{com}. replace offshwalt=25 if iscoco==2453
{txt}(48 real changes made)
{com}. replace offshwalt=48 if iscoco==2455
{txt}(16 real changes made)
{com}. replace offshwalt=47 if iscoco==3111
{txt}(106 real changes made)
{com}. replace offshwalt=47 if iscoco==3113
{txt}(83 real changes made)
{com}. replace offshwalt=47 if iscoco==3114
{txt}(53 real changes made)
{com}. replace offshwalt=72 if iscoco==3115
{txt}(136 real changes made)
{com}. replace offshwalt=47 if iscoco==3116
{txt}(0 real changes made)
{com}. replace offshwalt=94 if iscoco==3118
{txt}(69 real changes made)
{com}. replace offshwalt=54 if iscoco==3119
{txt}(238 real changes made)
{com}. replace offshwalt=75 if iscoco==3121
{txt}(245 real changes made)
{com}. replace offshwalt=84 if iscoco==3122
{txt}(12 real changes made)
{com}. replace offshwalt=68 if iscoco==3123
{txt}(0 real changes made)
{com}. replace offshwalt=36 if iscoco==3131
{txt}(77 real changes made)
{com}. replace offshwalt=36 if iscoco==3132
{txt}(1 real change made)
{com}. replace offshwalt=46 if iscoco==3133
{txt}(22 real changes made)
{com}. replace offshwalt=34 if iscoco==3139
{txt}(7 real changes made)
{com}. replace offshwalt=52 if iscoco==3141
{txt}(0 real changes made)
{com}. replace offshwalt=60 if iscoco==3152
{txt}(144 real changes made)
{com}. replace offshwalt=55 if iscoco==3211
{txt}(72 real changes made)
{com}. replace offshwalt=55 if iscoco==3212
{txt}(8 real changes made)
{com}. replace offshwalt=44 if iscoco==3213
{txt}(5 real changes made)
{com}. replace offshwalt=32 if iscoco==3228
{txt}(83 real changes made)
{com}. replace offshwalt=51 if iscoco==3411
{txt}(9 real changes made)
{com}. replace offshwalt=85 if iscoco==3412
{txt}(59 real changes made)
{com}. replace offshwalt=50 if iscoco==3414
{txt}(3 real changes made)
{com}. replace offshwalt=55 if iscoco==3416
{txt}(68 real changes made)
{com}. replace offshwalt=59 if iscoco==3419
{txt}(616 real changes made)
{com}. replace offshwalt=51 if iscoco==3421
{txt}(6 real changes made)
{com}. replace offshwalt=48 if iscoco==3422
{txt}(0 real changes made)
{com}. replace offshwalt=38 if iscoco==3431
{txt}(428 real changes made)
{com}. replace offshwalt=51 if iscoco==3432
{txt}(291 real changes made)
{com}. replace offshwalt=84 if iscoco==3433
{txt}(303 real changes made)
{com}. replace offshwalt=84 if iscoco==3434
{txt}(0 real changes made)
{com}. replace offshwalt=54 if iscoco==3439
{txt}(0 real changes made)
{com}. replace offshwalt=100 if iscoco==3442
{txt}(59 real changes made)
{com}. replace offshwalt=85 if iscoco==3471
{txt}(91 real changes made)
{com}. replace offshwalt=30 if iscoco==3472
{txt}(1 real change made)
{com}. replace offshwalt=95 if iscoco==4111
{txt}(15 real changes made)
{com}. replace offshwalt=94 if iscoco==4112
{txt}(0 real changes made)
{com}. replace offshwalt=100 if iscoco==4113
{txt}(12 real changes made)
{com}. replace offshwalt=71 if iscoco==4114
{txt}(0 real changes made)
{com}. replace offshwalt=38 if iscoco==4115
{txt}(174 real changes made)
{com}. replace offshwalt=84 if iscoco==4121
{txt}(194 real changes made)
{com}. replace offshwalt=54 if iscoco==4122
{txt}(331 real changes made)
{com}. replace offshwalt=31 if iscoco==4131
{txt}(247 real changes made)
{com}. replace offshwalt=67 if iscoco==4132
{txt}(131 real changes made)
{com}. replace offshwalt=67 if iscoco==4133
{txt}(125 real changes made)
{com}. replace offshwalt=46 if iscoco==4141
{txt}(55 real changes made)
{com}. replace offshwalt=26 if iscoco==4142
{txt}(124 real changes made)
{com}. replace offshwalt=95 if iscoco==4143
{txt}(1 real change made)
{com}. replace offshwalt=54 if iscoco==4144
{txt}(0 real changes made)
{com}. replace offshwalt=54 if iscoco==4190
{txt}(766 real changes made)
{com}. replace offshwalt=94 if iscoco==4211
{txt}(128 real changes made)
{com}. replace offshwalt=54 if iscoco==4214
{txt}(0 real changes made)
{com}. replace offshwalt=65 if iscoco==4215
{txt}(5 real changes made)
{com}. replace offshwalt=72 if iscoco==4221
{txt}(48 real changes made)
{com}. replace offshwalt=54 if iscoco==4222
{txt}(84 real changes made)
{com}. replace offshwalt=50 if iscoco==4223
{txt}(94 real changes made)
{com}. replace offshwalt=86 if iscoco==5113
{txt}(10 real changes made)
{com}. replace offshwalt=59 if iscoco==6142
{txt}(0 real changes made)
{com}. replace offshwalt=36 if iscoco==7111
{txt}(1 real change made)
{com}. replace offshwalt=35 if iscoco==7112
{txt}(0 real changes made)
{com}. replace offshwalt=36 if iscoco==7113
{txt}(14 real changes made)
{com}. replace offshwalt=65 if iscoco==7211
{txt}(2 real changes made)
{com}. replace offshwalt=69 if iscoco==7212
{txt}(67 real changes made)
{com}. replace offshwalt=70 if iscoco==7213
{txt}(41 real changes made)
{com}. replace offshwalt=70 if iscoco==7222
{txt}(54 real changes made)
{com}. replace offshwalt=68 if iscoco==7223
{txt}(157 real changes made)
{com}. replace offshwalt=68 if iscoco==7224
{txt}(1 real change made)
{com}. replace offshwalt=26 if iscoco==7311
{txt}(87 real changes made)
{com}. replace offshwalt=64 if iscoco==7313
{txt}(6 real changes made)
{com}. replace offshwalt=69 if iscoco==7321
{txt}(7 real changes made)
{com}. replace offshwalt=69 if iscoco==7322
{txt}(8 real changes made)
{com}. replace offshwalt=68 if iscoco==7323
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==7324
{txt}(1 real change made)
{com}. replace offshwalt=60 if iscoco==7331
{txt}(4 real changes made)
{com}. replace offshwalt=75 if iscoco==7332
{txt}(1 real change made)
{com}. replace offshwalt=59 if iscoco==7341
{txt}(70 real changes made)
{com}. replace offshwalt=59 if iscoco==7342
{txt}(0 real changes made)
{com}. replace offshwalt=59 if iscoco==7343
{txt}(1 real change made)
{com}. replace offshwalt=34 if iscoco==7344
{txt}(2 real changes made)
{com}. replace offshwalt=59 if iscoco==7345
{txt}(11 real changes made)
{com}. replace offshwalt=75 if iscoco==7346
{txt}(6 real changes made)
{com}. replace offshwalt=68 if iscoco==7413
{txt}(6 real changes made)
{com}. replace offshwalt=27 if iscoco==7414
{txt}(0 real changes made)
{com}. replace offshwalt=55 if iscoco==7415
{txt}(0 real changes made)
{com}. replace offshwalt=43 if iscoco==7421
{txt}(0 real changes made)
{com}. replace offshwalt=57 if iscoco==7422
{txt}(125 real changes made)
{com}. replace offshwalt=57 if iscoco==7423
{txt}(37 real changes made)
{com}. replace offshwalt=66 if iscoco==7424
{txt}(3 real changes made)
{com}. replace offshwalt=75 if iscoco==7431
{txt}(0 real changes made)
{com}. replace offshwalt=75 if iscoco==7432
{txt}(6 real changes made)
{com}. replace offshwalt=75 if iscoco==7433
{txt}(23 real changes made)
{com}. replace offshwalt=73 if iscoco==7434
{txt}(0 real changes made)
{com}. replace offshwalt=75 if iscoco==7435
{txt}(0 real changes made)
{com}. replace offshwalt=75 if iscoco==7436
{txt}(24 real changes made)
{com}. replace offshwalt=57 if iscoco==7437
{txt}(21 real changes made)
{com}. replace offshwalt=75 if iscoco==7441
{txt}(0 real changes made)
{com}. replace offshwalt=72 if iscoco==7442
{txt}(20 real changes made)
{com}. replace offshwalt=36 if iscoco==8111
{txt}(0 real changes made)
{com}. replace offshwalt=36 if iscoco==8112
{txt}(2 real changes made)
{com}. replace offshwalt=36 if iscoco==8113
{txt}(0 real changes made)
{com}. replace offshwalt=59 if iscoco==8121
{txt}(11 real changes made)
{com}. replace offshwalt=68 if iscoco==8122
{txt}(26 real changes made)
{com}. replace offshwalt=70 if iscoco==8123
{txt}(1 real change made)
{com}. replace offshwalt=68 if iscoco==8124
{txt}(3 real changes made)
{com}. replace offshwalt=69 if iscoco==8131
{txt}(1 real change made)
{com}. replace offshwalt=68 if iscoco==8139
{txt}(0 real changes made)
{com}. replace offshwalt=57 if iscoco==8141
{txt}(8 real changes made)
{com}. replace offshwalt=68 if iscoco==8142
{txt}(2 real changes made)
{com}. replace offshwalt=68 if iscoco==8143
{txt}(11 real changes made)
{com}. replace offshwalt=68 if iscoco==8151
{txt}(1 real change made)
{com}. replace offshwalt=70 if iscoco==8152
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8153
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8154
{txt}(0 real changes made)
{com}. replace offshwalt=29 if iscoco==8155
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8159
{txt}(95 real changes made)
{com}. replace offshwalt=42 if iscoco==8161
{txt}(13 real changes made)
{com}. replace offshwalt=55 if iscoco==8162
{txt}(0 real changes made)
{com}. replace offshwalt=29 if iscoco==8163
{txt}(27 real changes made)
{com}. replace offshwalt=64 if iscoco==8171
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8172
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8211
{txt}(128 real changes made)
{com}. replace offshwalt=68 if iscoco==8212
{txt}(5 real changes made)
{com}. replace offshwalt=68 if iscoco==8221
{txt}(6 real changes made)
{com}. replace offshwalt=35 if iscoco==8222
{txt}(1 real change made)
{com}. replace offshwalt=68 if iscoco==8223
{txt}(19 real changes made)
{com}. replace offshwalt=41 if iscoco==8224
{txt}(0 real changes made)
{com}. replace offshwalt=29 if iscoco==8229
{txt}(2 real changes made)
{com}. replace offshwalt=69 if iscoco==8231
{txt}(11 real changes made)
{com}. replace offshwalt=68 if iscoco==8232
{txt}(86 real changes made)
{com}. replace offshwalt=57 if iscoco==8240
{txt}(3 real changes made)
{com}. replace offshwalt=58 if iscoco==8251
{txt}(0 real changes made)
{com}. replace offshwalt=59 if iscoco==8252
{txt}(3 real changes made)
{com}. replace offshwalt=68 if iscoco==8253
{txt}(14 real changes made)
{com}. replace offshwalt=75 if iscoco==8261
{txt}(9 real changes made)
{com}. replace offshwalt=75 if iscoco==8262
{txt}(0 real changes made)
{com}. replace offshwalt=75 if iscoco==8263
{txt}(0 real changes made)
{com}. replace offshwalt=75 if iscoco==8264
{txt}(64 real changes made)
{com}. replace offshwalt=75 if iscoco==8265
{txt}(0 real changes made)
{com}. replace offshwalt=75 if iscoco==8266
{txt}(2 real changes made)
{com}. replace offshwalt=75 if iscoco==8269
{txt}(0 real changes made)
{com}. replace offshwalt=27 if iscoco==8271
{txt}(16 real changes made)
{com}. replace offshwalt=68 if iscoco==8272
{txt}(7 real changes made)
{com}. replace offshwalt=68 if iscoco==8273
{txt}(0 real changes made)
{com}. replace offshwalt=30 if iscoco==8274
{txt}(11 real changes made)
{com}. replace offshwalt=31 if iscoco==8275
{txt}(68 real changes made)
{com}. replace offshwalt=68 if iscoco==8276
{txt}(3 real changes made)
{com}. replace offshwalt=27 if iscoco==8277
{txt}(3 real changes made)
{com}. replace offshwalt=68 if iscoco==8278
{txt}(16 real changes made)
{com}. replace offshwalt=66 if iscoco==8281
{txt}(2 real changes made)
{com}. replace offshwalt=66 if iscoco==8282
{txt}(7 real changes made)
{com}. replace offshwalt=66 if iscoco==8283
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8284
{txt}(11 real changes made)
{com}. replace offshwalt=57 if iscoco==8285
{txt}(0 real changes made)
{com}. replace offshwalt=64 if iscoco==8286
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8290
{txt}(123 real changes made)
{com}. replace offshwalt=34 if iscoco==8340
{txt}(9 real changes made)
{com}. replace offshwalt=95 if iscoco==9113
{txt}(0 real changes made)
{com}. replace offshwalt=64 if iscoco==9321
{txt}(0 real changes made)
{com}. replace offshwalt=29 if iscoco==9333
{txt}(0 real changes made)
{com}. replace offshwalt=55 if iscoco==1227
{txt}(61 real changes made)
{com}. replace offshwalt=89 if iscoco==2121
{txt}(0 real changes made)
{com}. replace offshwalt=100 if iscoco==2132
{txt}(0 real changes made)
{com}. replace offshwalt=70 if iscoco==2145
{txt}(274 real changes made)
{com}. replace offshwalt=71 if iscoco==2149
{txt}(0 real changes made)
{com}. replace offshwalt=81 if iscoco==2211
{txt}(0 real changes made)
{com}. replace offshwalt=46 if iscoco==2412
{txt}(45 real changes made)
{com}. replace offshwalt=50 if iscoco==2419
{txt}(0 real changes made)
{com}. replace offshwalt=33 if iscoco==2432
{txt}(20 real changes made)
{com}. replace offshwalt=89 if iscoco==2441
{txt}(39 real changes made)
{com}. replace offshwalt=48 if iscoco==2455
{txt}(0 real changes made)
{com}. replace offshwalt=72 if iscoco==3115
{txt}(0 real changes made)
{com}. replace offshwalt=54 if iscoco==3119
{txt}(0 real changes made)
{com}. replace offshwalt=46 if iscoco==3133
{txt}(0 real changes made)
{com}. replace offshwalt=34 if iscoco==3139
{txt}(0 real changes made)
{com}. replace offshwalt=34 if iscoco==3224
{txt}(35 real changes made)
{com}. replace offshwalt=51 if iscoco==3411
{txt}(0 real changes made)
{com}. replace offshwalt=25 if iscoco==3415
{txt}(138 real changes made)
{com}. replace offshwalt=50 if iscoco==3417
{txt}(5 real changes made)
{com}. replace offshwalt=59 if iscoco==3419
{txt}(0 real changes made)
{com}. replace offshwalt=51 if iscoco==3432
{txt}(0 real changes made)
{com}. replace offshwalt=54 if iscoco==3439
{txt}(0 real changes made)
{com}. replace offshwalt=90 if iscoco==3460
{txt}(450 real changes made)
{com}. replace offshwalt=54 if iscoco==4122
{txt}(0 real changes made)
{com}. replace offshwalt=31 if iscoco==4131
{txt}(0 real changes made)
{com}. replace offshwalt=67 if iscoco==4132
{txt}(0 real changes made)
{com}. replace offshwalt=46 if iscoco==4141
{txt}(0 real changes made)
{com}. replace offshwalt=26 if iscoco==4142
{txt}(0 real changes made)
{com}. replace offshwalt=54 if iscoco==4222
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==7141
{txt}(131 real changes made)
{com}. replace offshwalt=68 if iscoco==7224
{txt}(0 real changes made)
{com}. replace offshwalt=65 if iscoco==7241
{txt}(297 real changes made)
{com}. replace offshwalt=34 if iscoco==7344
{txt}(0 real changes made)
{com}. replace offshwalt=72 if iscoco==7442
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8122
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8139
{txt}(0 real changes made)
{com}. replace offshwalt=42 if iscoco==8161
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8211
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8223
{txt}(0 real changes made)
{com}. replace offshwalt=41 if iscoco==8224
{txt}(0 real changes made)
{com}. replace offshwalt=57 if iscoco==8240
{txt}(0 real changes made)
{com}. replace offshwalt=58 if iscoco==8251
{txt}(0 real changes made)
{com}. replace offshwalt=75 if iscoco==8269
{txt}(0 real changes made)
{com}. replace offshwalt=66 if iscoco==8281
{txt}(0 real changes made)
{com}. replace offshwalt=66 if iscoco==8283
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8284
{txt}(0 real changes made)
{com}. replace offshwalt=68 if iscoco==8290
{txt}(0 real changes made)
{com}. replace offshwalt=70 if iscoco==9322
{txt}(0 real changes made)
{com}. {c )-}
. 
. * Create Ordinal Offshwalt Variable
. {c -(}
. gen offshwalt4=.
{txt}(56,109 missing values generated)
{com}. replace offshwalt4=4 if offshwalt<.
{txt}(26,616 real changes made)
{com}. replace offshwalt4=3 if offshwalt<75
{txt}(24,128 real changes made)
{com}. replace offshwalt4=2 if offshwalt<50
{txt}(17,138 real changes made)
{com}. replace offshwalt4=1 if offshwalt<25
{txt}(14,693 real changes made)
{com}. label var offshwalt4 "offshwalt ordinal (4 categories)"
. {c )-}
. 
. * Binary Offshwalt Variable
. gen offshwalt2=offshwalt4>1
. replace offshwalt2=. if offshwalt==.
{txt}(29,493 real changes made, 29,493 to missing)
{com}. label var offshwalt2 "offshwalt binary: =1 if offshwalt>=25 if offshwalt"
. {c )-}
. *******************************************************************************
. *****4. Skill specificity provided Iversen, Cusack and Rehm (2011) ************
. *******************************************************************************
. {c -(}
. {c -(}
. */ This code is provided by Torben Iversen; Thomas Cusack; Philipp Rehm, 2011: Risks at Work The Demand and Supply Sides of Government Redistribution
. */ See: Raw data\Occupations\Measuring_skill-specificity Iversen, Cusack and Rehm (2011).xlsx column J Relative skill specificity  
. {c -(}
. gen relskillspec=.      
{txt}(56,109 missing values generated)
{com}.         replace relskillspec=6.168528055        if iscoco2==11
{txt}(53 real changes made)
{com}.         replace relskillspec=2.840445869        if iscoco2==12
{txt}(953 real changes made)
{com}.         replace relskillspec=1.612754672        if iscoco2==13
{txt}(495 real changes made)
{com}.         replace relskillspec=3.94911483         if iscoco2==21
{txt}(1,553 real changes made)
{com}.         replace relskillspec=2.875304662        if iscoco2==22
{txt}(432 real changes made)
{com}.         replace relskillspec=1.298205499        if iscoco2==23
{txt}(1,304 real changes made)
{com}.         replace relskillspec=3.38271403         if iscoco2==24
{txt}(2,213 real changes made)
{com}.         replace relskillspec=5.999693379        if iscoco2==31
{txt}(1,394 real changes made)
{com}.         replace relskillspec=5.599717306        if iscoco2==32
{txt}(1,055 real changes made)
{com}.         replace relskillspec=2.435374975        if iscoco2==33
{txt}(812 real changes made)
{com}.         replace relskillspec=3.582262408        if iscoco2==34
{txt}(3,295 real changes made)
{com}.         replace relskillspec=1.803739154        if iscoco2==41
{txt}(2,175 real changes made)
{com}.         replace relskillspec=3.899735492        if iscoco2==42
{txt}(413 real changes made)
{com}.         replace relskillspec=3.020082744        if iscoco2==51
{txt}(2,258 real changes made)
{com}.         replace relskillspec=0.787535801        if iscoco2==52
{txt}(1,041 real changes made)
{com}.         replace relskillspec=4.674256488        if iscoco2==61
{txt}(316 real changes made)
{com}.         replace relskillspec=3.91546829         if iscoco2==71
{txt}(1,112 real changes made)
{com}.         replace relskillspec=3.842807254        if iscoco2==72
{txt}(1,314 real changes made)
{com}.         replace relskillspec=20.41910075        if iscoco2==73
{txt}(216 real changes made)
{com}.         replace relskillspec=9.446026838        if iscoco2==74
{txt}(395 real changes made)
{com}.         replace relskillspec=25.06473567        if iscoco2==81
{txt}(201 real changes made)
{com}.         replace relskillspec=12.27676336        if iscoco2==82
{txt}(620 real changes made)
{com}.         replace relskillspec=3.672294237        if iscoco2==83
{txt}(856 real changes made)
{com}.         replace relskillspec=7.39344664         if iscoco2==91
{txt}(1,320 real changes made)
{com}.         replace relskillspec=7.384150506        if iscoco2==92
{txt}(69 real changes made)
{com}.         replace relskillspec=6.460338111        if iscoco2==93
{txt}(660 real changes made)
{com}.         label var relskillspec "Relative skill specificity , Iversen"
. {c )-}
.         
. {c )-}
. 
. {c )-}
. 
. *******************************************************************************
. * 5 task categories Kurer (2020)
. *******************************************************************************
. {c -(}
. rename iscoco iscoco_withoutchange
{res}{com}. gen iscoco=iscoco_withoutchange
{txt}(29,493 missing values generated)
{com}.     
. {c -(}
. *generating 3 task categories
. gen task = .
{txt}(56,109 missing values generated)
{com}. gen isco=iscoco
{txt}(29,493 missing values generated)
{com}. replace task = 1 if inlist(isco, 2411, 2431, 2441, 3411, 3471)
{txt}(242 real changes made)
{com}. replace task = 1 if inrange(isco, 2100, 2213)
{txt}(1,599 real changes made)
{com}. replace task = 1 if inrange(isco, 2443, 2444)
{txt}(50 real changes made)
{com}. replace task = 1 if inrange(isco, 2446, 2452)
{txt}(581 real changes made)
{com}. replace task = 1 if inrange(isco, 3100, 3212)
{txt}(1,474 real changes made)
{com}. replace task = 1 if inrange(isco, 3433, 3440)
{txt}(303 real changes made)
{com}. replace task = 1 if inrange(isco, 3442, 3444)
{txt}(226 real changes made)
{com}. 
. replace task = 2 if inlist(isco, 2442, 2445, 3226, 3432, 3441)
{txt}(519 real changes made)
{com}. replace task = 2 if inrange(isco, 1000, 1319)
{txt}(1,501 real changes made)
{com}. replace task = 2 if inrange(isco, 2220, 2410)
{txt}(1,714 real changes made)
{com}. replace task = 2 if inrange(isco, 2412, 2430)
{txt}(723 real changes made)
{com}. replace task = 2 if inrange(isco, 2432, 2440)
{txt}(20 real changes made)
{com}. replace task = 2 if inrange(isco, 2453, 2470)
{txt}(582 real changes made)
{com}. replace task = 2 if inrange(isco, 3213, 3220)
{txt}(5 real changes made)
{com}. replace task = 2 if inrange(isco, 3222, 3224)
{txt}(52 real changes made)
{com}. replace task = 2 if inrange(isco, 3229, 3410)
{txt}(1,510 real changes made)
{com}. replace task = 2 if inrange(isco, 3412, 3429)
{txt}(1,116 real changes made)
{com}. replace task = 2 if inrange(isco, 3449, 3470)
{txt}(762 real changes made)
{com}. replace task = 2 if inrange(isco, 3472, 3480)
{txt}(69 real changes made)
{com}. 
. replace task = 3 if isco==4223
{txt}(94 real changes made)
{com}. replace task = 3 if inrange(isco, 3430, 3431)
{txt}(428 real changes made)
{com}. replace task = 3 if inrange(isco, 4000, 4195)
{txt}(2,175 real changes made)
{com}. replace task = 3 if inrange(isco, 4210, 4215)
{txt}(187 real changes made)
{com}. 
. replace task = 4 if inlist(isco, 7124, 8340, 9120, 9133)
{txt}(76 real changes made)
{com}. replace task = 4 if inrange(isco, 1, 110) /* departing from oesch, including 110 (armed forces). this is the actual intention of 1-100 */
{txt}(91 real changes made)
{com}. replace task = 4 if inrange(isco, 6100, 7113)
{txt}(331 real changes made)
{com}. replace task = 4 if inrange(isco, 7210, 8290)
{txt}(2,746 real changes made)
{com}. replace task = 4 if inrange(isco, 9000, 9001)
{txt}(0 real changes made)
{com}. replace task = 4 if inrange(isco, 9150, 9151)
{txt}(77 real changes made)
{com}. replace task = 4 if inrange(isco, 9153, 9161)
{txt}(13 real changes made)
{com}. replace task = 4 if inrange(isco, 9200, 9311)
{txt}(70 real changes made)
{com}. 
. replace task = 5 if inlist(isco, 5122, 5143, 9002, 9162)
{txt}(400 real changes made)
{com}. replace task = 5 if inrange(isco, 7120, 7123)
{txt}(130 real changes made)
{com}. replace task = 5 if inrange(isco, 7129, 7143)
{txt}(900 real changes made)
{com}. replace task = 5 if inrange(isco, 8300, 8334)
{txt}(847 real changes made)
{com}. replace task = 5 if inrange(isco, 9130, 9132)
{txt}(881 real changes made)
{com}. replace task = 5 if inrange(isco, 9140, 9142)
{txt}(295 real changes made)
{com}. replace task = 5 if inrange(isco, 9312, 9313)
{txt}(58 real changes made)
{com}. 
. replace task = 6 if inlist(isco, 3221, 3225, 4200, 9152)
{txt}(51 real changes made)
{com}. replace task = 6 if inrange(isco, 3227, 3228)
{txt}(83 real changes made)
{com}. replace task = 6 if inrange(isco, 4220, 4222)
{txt}(132 real changes made)
{com}. replace task = 6 if inrange(isco, 5000, 5121)
{txt}(182 real changes made)
{com}. replace task = 6 if inrange(isco, 5123, 5142)
{txt}(1,397 real changes made)
{com}. replace task = 6 if inrange(isco, 5149, 5220)
{txt}(1,320 real changes made)
{com}. replace task = 6 if inrange(isco, 9003, 9005)
{txt}(0 real changes made)
{com}. replace task = 6 if inrange(isco, 9100, 9113)
{txt}(3 real changes made)
{com}. replace task = 6 if inrange(isco, 9320, 9333) /* departing from oesch, including 9333 (transport labourers, animal vehicles) */
{txt}(601 real changes made)
{com}. 
. * add isco categories 2000 and 3000
. * officially not defined, thus not part of oesch's categories
. 
. replace task = 1 if inlist(isco, 2000, 3000)
{txt}(0 real changes made)
{com}. 
. * task3
. 
. gen task3 = .
{txt}(56,109 missing values generated)
{com}. replace task3 = 1 if task==1 | task==2 // NRM
{txt}(13,048 real changes made)
{com}. replace task3 = 2 if task==3 | task==4 // R
{txt}(6,288 real changes made)
{com}. replace task3 = 3 if task==5 | task==6 // NRC
{txt}(7,280 real changes made)
{com}. tab task3

      {txt}task3 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}     13,048       49.02       49.02
{txt}          2 {c |}{res}      6,288       23.62       72.65
{txt}          3 {c |}{res}      7,280       27.35      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     26,616      100.00
{com}. 
. 
. drop isco iscoco
. rename iscoco_withoutchange iscoco
{res}{com}. 
. {c )-}
. 
. {c )-}
. {c )-}
. ************************************************************************
. * Generate a dummy to summarize the risk of automation 
. gen task3cog1=(task3==1) // dummy for non routine cognitive work - 3 task approach
. replace task3cog1=. if task3==.
{txt}(29,493 real changes made, 29,493 to missing)
{com}. gen task3cog2and3=(task3==2 | task3==3) // dummy for  routine cognitive work - 3 task approach
. replace task3cog2and3=. if task3==.
{txt}(29,493 real changes made, 29,493 to missing)
{com}. 
. {c )-}
. ************************************************************************
. ********************* E. Switching Vote ********************************
. ************************************************************************
. {c -(}
. // Generating the years I am interested
. {c -(}
. gen pres12=. 
{txt}(56,109 missing values generated)
{com}. replace pres12=1 if establishment_left2==1 & syear==2014
{txt}(3,706 real changes made)
{com}. replace pres12=2 if establishment_right==1 & syear==2014
{txt}(4,579 real changes made)
{com}. replace pres12=3 if populist==1 & syear==2014
{txt}(205 real changes made)
{com}. replace pres12=4 if plh0012_h>0 & populist~=1 & establishment_right~=1 & establishment_left2~=1 & syear==2014
{txt}(3,513 real changes made)
{com}. 
. 
. gen pres16=. 
{txt}(56,109 missing values generated)
{com}. replace pres16=1 if establishment_left2==1 & syear==2018
{txt}(3,112 real changes made)
{com}. replace pres16=2 if establishment_right==1 & syear==2018
{txt}(3,907 real changes made)
{com}. replace pres16=3 if populist==1 & syear==2018
{txt}(692 real changes made)
{com}. replace pres16=4 if plh0012_h>0 & populist~=1 & establishment_right~=1 & establishment_left2~=1 & syear==2018
{txt}(3,555 real changes made)
{com}. 
. gen vot_12_14=pres12 if syear==2014
{txt}(44,106 missing values generated)
{com}. replace vot_12_14=. if pres12~=1 & pres12~=2 & pres12~=3   & pres12~=4
{txt}(0 real changes made)
{com}. by pid, sort: egen vot12_14=total(vot_12_14)
. 
. gen switching2 = . 
{txt}(56,109 missing values generated)
{com}. replace switching2 = 1 if vot12_14==1 & pres16==3 // left to pop
{txt}(28 real changes made)
{com}. replace switching2 = 0 if vot12_14==3 & pres16==3 // always pop
{txt}(61 real changes made)
{com}. replace switching2 = 0 if vot12_14==2 //Republicans in 2012
{txt}(7,670 real changes made)
{com}. replace switching2 = 0 if vot12_14==4 //Other
{txt}(5,882 real changes made)
{com}. replace switching2 = 0 if vot12_14==3 & pres16~=3 // Populist to anything except Rep
{txt}(286 real changes made)
{com}. 
. gen switching2_broad = . 
{txt}(56,109 missing values generated)
{com}. replace switching2_broad = 1 if vot12_14~=3 & pres16==3 // no pop to pop
{txt}(631 real changes made)
{com}. replace switching2_broad = 0 if vot12_14==3 & pres16==3 // always pop
{txt}(61 real changes made)
{com}. replace switching2_broad = 0 if pres16~=3 & syear==2018
{txt}(28,735 real changes made)
{com}. 
. gen switching2_r = . 
{txt}(56,109 missing values generated)
{com}. replace switching2_r = 1 if vot12_14==2 & pres16==3 // left to pop
{txt}(78 real changes made)
{com}. replace switching2_r = 0 if vot12_14==3 & pres16==3 // always pop
{txt}(61 real changes made)
{com}. replace switching2_r = 0 if vot12_14==1 //Republicans in 2012
{txt}(6,180 real changes made)
{com}. replace switching2_r = 0 if vot12_14==4 //Other
{txt}(5,882 real changes made)
{com}. replace switching2_r = 0 if vot12_14==3 & pres16~=3 // Populist to anything except Rep
{txt}(286 real changes made)
{com}. 
. 
. 
. {c )-}
. {c )-}
. *############################################
. * Saving the data
. *############################################
. {c -(}
. // Label
. lab var switching2 "Left to Pop Right"
. lab var switching2_r "Right to AfD"
. lab var switching2_broad "Switching Vote"
. lab var rti "RTI Index"
. lab var female "Female"
. lab var age "Age"
. lab var educ "Education"
. lab var high "High-Skilled"
. lab var unemployed "Unemployed"
. lab var foreign "Foreign born"
. lab var nonrelig "Non-Believer"
. lab var offshwalt2 "Offshorability"
. lab var relskillspec "Skill-Specificity"
. lab var t2 "Task-Tech"
. lab var t3 "Task-Inter"
. lab var task3cog1 "Non-Routine"
. lab var task3cog2and3 "Routine"
. lab var sampreg "Region (West 1 - East 2)"
. 
.         
. {c )-}
. keep switching2 switching2_r switching2_broad rti female age educ high unemployed nonrelig offshwalt2 relskillspec t2 t3 task3cog1 task3cog2and3 sampreg pid syear income rincome year  phrf   foreign
. 
. save "Data\SOEP.dta", replace
{txt}{p 0 4 2}
file {bf}
Data\SOEP.dta{rm}
saved
{p_end}
{com}. 
. {c )-}
{txt}
{com}. *##########################################
. * Alternatively load prepared data
. *##########################################
. {c -(}
. use "Data\SOEP.dta", clear      
. {c )-}       
{txt}
{com}. *##########################################     
. * Analysis      
. *##########################################     
. {c -(}       
. {c -(}       
.         
. // How is the data?     
. xtset pid syear 
{res}
{col 1}{txt:Panel variable: }{res:pid}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:syear}{txt:, }{res:{bind:2014}}{txt: to }{res:{bind:2018}}{txt:, but with gaps}{p_end}
{txt}{col 10}Delta: {res}1 unit
{com}. xtdes   

     {txt}pid:  {res}901{txt}, {res}1501{txt}, ..., {res}39238301                          {txt}n ={res}      38965
   {txt}syear:  {res}2014, 2018, ..., 2018                             {txt}T ={res}          2
           {txt}Delta(syear) = {res}1 unit
           {txt}Span(syear)  = {res}5 periods
           {txt}(pid*syear uniquely identifies each observation)

Distribution of T_i:   min      5%     25%       50%       75%     95%     max
                    {res}     1       1       1         1         2       2       2

{txt}{col 6}Freq.  Percent    Cum. {c |}  Pattern*
 {hline 27}{c +}{c -}{c -}{hline 8}
{res}    17144     44.00   44.00{txt} {c |}  {res}11
    12283     31.52   75.52{txt} {c |}  {res}.1
     9538     24.48  100.00{txt} {c |}  {res}1.
{txt} {hline 27}{c +}{c -}{c -}{hline 8}
{res}    38965    100.00        {txt} {c |}  {res}XX
{txt} {hline 27}{c BT}{c -}{c -}{hline 8}
 *Each column represents 4 periods.

{com}.         
. * Figure        
. {c -(}       
. // Graph style  
. // Graph style          
. grstyle clear           
. set scheme s2color              
. grstyle init            
{res}{com}. grstyle set plain, nogrid               
. grstyle color background white          
.                 
. // Figure 3: The effect of exposure to automation on vote-switching. [German part]              
. // Margins              
. {c -(}               
. 
. 
.          
. logit  switching2   rti  female age  foreign   income offshwalt2      [pw=phrf] if year==2018 

{txt}note: {bf:foreign} != 0 predicts failure perfectly;
      {bf:foreign} omitted and 62 obs not used.

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-267644.88}  
Iteration 1:{space 3}log pseudolikelihood = {res:-246149.68}  
Iteration 2:{space 3}log pseudolikelihood = {res:-236302.79}  
Iteration 3:{space 3}log pseudolikelihood = {res:-236116.66}  
Iteration 4:{space 3}log pseudolikelihood = {res:-236115.89}  
Iteration 5:{space 3}log pseudolikelihood = {res:-236115.89}  
{res}
{txt}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,777}
{txt}{col 57}{lalign 13:Wald chi2({res:5})}{col 70} = {res}{ralign 6:17.73}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0033}
{txt}Log pseudolikelihood = {res:-236115.89}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1178}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  switching2{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rti {c |}{col 14}{res}{space 2} 1.017934{col 26}{space 2} .4200115{col 37}{space 1}    2.42{col 46}{space 3}0.015{col 54}{space 4} .1947268{col 67}{space 3} 1.841142
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.5232226{col 26}{space 2} .8798132{col 37}{space 1}   -0.59{col 46}{space 3}0.552{col 54}{space 4}-2.247625{col 67}{space 3}  1.20118
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0329574{col 26}{space 2} .0289082{col 37}{space 1}   -1.14{col 46}{space 3}0.254{col 54}{space 4}-.0896164{col 67}{space 3} .0237015
{txt}{space 5}foreign {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 6}income {c |}{col 14}{res}{space 2} .0001009{col 26}{space 2} .0000998{col 37}{space 1}    1.01{col 46}{space 3}0.312{col 54}{space 4}-.0000947{col 67}{space 3} .0002965
{txt}{space 2}offshwalt2 {c |}{col 14}{res}{space 2}-2.847035{col 26}{space 2} 1.470596{col 37}{space 1}   -1.94{col 46}{space 3}0.053{col 54}{space 4} -5.72935{col 67}{space 3} .0352801
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-2.112584{col 26}{space 2} 1.387122{col 37}{space 1}   -1.52{col 46}{space 3}0.128{col 54}{space 4}-4.831292{col 67}{space 3} .6061249
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. 
.                 
. margins, atmeans at(rti=(-1.52(0.05)2.24))              
{res}
{txt}Adjusted predictions{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,777}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(switching2), predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-1.52}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:2._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-1.47}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:3._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-1.42}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:4._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-1.37}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:5._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-1.32}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:6._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-1.27}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:7._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-1.22}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:8._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-1.17}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:9._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-1.12}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:10._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-1.07}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:11._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-1.02}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:12._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.97}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:13._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.92}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:14._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.87}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:15._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.82}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:16._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.77}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:17._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.72}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:18._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.67}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:19._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.62}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:20._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.57}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:21._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.52}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:22._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.47}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:23._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.42}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:24._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.37}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:25._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.32}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:26._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.27}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:27._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.22}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:28._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.17}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:29._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.12}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:30._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.07}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:31._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:-.02}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:32._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.03}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:33._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.08}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:34._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.13}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:35._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.18}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:36._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.23}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:37._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.28}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:38._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.33}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:39._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.38}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:40._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.43}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:41._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.48}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:42._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.53}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:43._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.58}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:44._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.63}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:45._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.68}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:46._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.73}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:47._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.78}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:48._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.83}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:49._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.88}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:50._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.93}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:51._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:.98}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:52._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.03}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:53._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.08}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:54._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.13}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:55._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.18}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:56._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.23}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:57._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.28}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:58._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.33}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:59._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.38}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:60._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.43}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:61._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.48}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:62._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.53}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:63._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.58}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:64._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.63}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:65._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.68}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:66._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.73}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:67._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.78}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:68._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.83}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:69._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.88}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:70._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.93}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:71._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:1.98}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:72._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:2.03}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:73._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:2.08}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:74._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:2.13}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:75._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:2.18}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}
{lalign 8:76._at: }{space 0}{lalign 10:rti} = {res:{ralign 8:2.23}}
{lalign 8:}{space 0}{lalign 10:female} = {res:{ralign 8:.4276016}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:age} = {res:{ralign 8:47.16494}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:foreign} = {res:{ralign 8:0}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:income} = {res:{ralign 8:3324.045}} {txt:(mean)}
{lalign 8:}{space 0}{lalign 10:offshwalt2} = {res:{ralign 8:.5717052}} {txt:(mean)}

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{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}.      marginsplot , recast(line) recastci(rline) ci1opts(fintensity(50) lpattern(dot)) xti(Risk of automation (RTI - Index)) yti(Predicted Probability of Switching (95% CI)) ti("Germany")                              saving("Figure/Germany.gph", replace)   
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:rti}{p_end}
{res}{txt}file {bf:Figure/Germany.gph} saved
{com}. {c )-}               
.                 
. graph combine "Figure/US.gph" "Figure/Germany.gph"              
{res}{com}.  graph export "Figure/Graph_US_Germany.pdf", as(pdf) replace            
{txt}{p 0 4 2}
file {bf}
Figure/Graph_US_Germany.pdf{rm}
saved as
PDF
format
{p_end}
{com}.                 
. {c )-}       
.                                         
. // Regressions                                  
. {c -(}                                       
. // table A4: Switching Vote (Only left) - Germany, IV - RTI                                     
. {c -(}                                       
. eststo clear                                    
. eststo: qui logit switching2 rti  female age   foreign high i.rincome   [pw=phrf]       if year==2018, robust                           
{txt}({res}est1{txt} stored)
{com}. eststo: qui logit switching2 rti  female age   foreign high i.rincome offshwalt2  i.sampreg [pw=phrf]   if year==2018, robust                           
{txt}({res}est2{txt} stored)
{com}. eststo: qui logit switching2 rti  female age   foreign high i.rincome offshwalt2 relskillspec  i.sampreg  [pw=phrf]     if year==2018   , robust                        
{txt}({res}est3{txt} stored)
{com}. eststo: qui logit switching2 rti  female age   foreign high i.rincome offshwalt2 relskillspec t2 t3 i.sampreg  [pw=phrf]        if year==2018   , robust                        
{txt}({res}est4{txt} stored)
{com}. esttab , replace label se title(Switching Vote (Only left), IV - RTI \label {c -(}tab:left{c )-})mti("+Demographic" "+Offshoring" "+Skill"  "All") compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(rti  female age    high offshwalt2 relskillspec t2 t3) scalars(N  r2_p aic) indicate( "Income = *income"  "Regional controls = *.sampreg")                                   
{res}
{txt}Switching Vote (Only left), IV - RTI \label {tab:left}
{txt}{hline 68}
{txt}                       (1)          (2)          (3)          (4)   
{txt}                 +Demogr~c    +Offsho~g       +Skill          All   
{txt}{hline 68}
{res}Left to Pop Ri~t                                                    {txt}
{txt}RTI Index       {res}     0.210        0.902***     0.981***     0.887***{txt}
                {res} {ralign 9:{txt:(}0.197{txt:)}}    {ralign 9:{txt:(}0.323{txt:)}}    {ralign 9:{txt:(}0.317{txt:)}}    {ralign 9:{txt:(}0.248{txt:)}}   {txt}
{txt}Female          {res}    -0.427       -0.501       -0.301       -0.168   {txt}
                {res} {ralign 9:{txt:(}0.949{txt:)}}    {ralign 9:{txt:(}0.887{txt:)}}    {ralign 9:{txt:(}0.876{txt:)}}    {ralign 9:{txt:(}0.837{txt:)}}   {txt}
{txt}Age             {res}    -0.029       -0.037       -0.047       -0.046   {txt}
                {res} {ralign 9:{txt:(}0.037{txt:)}}    {ralign 9:{txt:(}0.033{txt:)}}    {ralign 9:{txt:(}0.031{txt:)}}    {ralign 9:{txt:(}0.030{txt:)}}   {txt}
{txt}High-Skilled    {res}    -1.402       -1.464       -1.470       -1.447   {txt}
                {res} {ralign 9:{txt:(}1.030{txt:)}}    {ralign 9:{txt:(}1.462{txt:)}}    {ralign 9:{txt:(}1.359{txt:)}}    {ralign 9:{txt:(}1.265{txt:)}}   {txt}
{txt}Offshorability  {res}                 -2.773**     -3.049***    -3.131***{txt}
                {res}              {ralign 9:{txt:(}1.201{txt:)}}    {ralign 9:{txt:(}0.948{txt:)}}    {ralign 9:{txt:(}1.004{txt:)}}   {txt}
{txt}Skill-Specific~y{res}                               0.138**      0.129** {txt}
                {res}                           {ralign 9:{txt:(}0.055{txt:)}}    {ralign 9:{txt:(}0.058{txt:)}}   {txt}
{txt}Task-Tech       {res}                                            0.108   {txt}
                {res}                                        {ralign 9:{txt:(}0.959{txt:)}}   {txt}
{txt}Task-Inter      {res}                                           -0.664   {txt}
                {res}                                        {ralign 9:{txt:(}1.195{txt:)}}   {txt}
{txt}Income          {res}       Yes          Yes          Yes          Yes   {txt}
{txt}Regional contr~s{res}        No          Yes          Yes          Yes   {txt}
{txt}{hline 68}
{txt}Observations    {res}      1070         1070         1070         1066   {txt}
{txt}r2_p            {res}     0.067        0.182        0.202        0.210   {txt}
{txt}aic             {res}   4.5e+05      4.0e+05      3.9e+05      3.8e+05   {txt}
{txt}{hline 68}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}. esttab using "Table\SDU.tex", replace label se title(Switching Vote (Only left), IV - RTI, Germany \label {c -(}tab:left{c )-})mti("+Demographic" "+Offshoring" "+Skill"  "All")  compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(rti  female age    high offshwalt2 relskillspec t2 t3) scalars( "N Observations" "r2_p R$^2$" "aic AIC" )indicate( "Income = *income"  "Regional controls = *.sampreg")                                       
{res}{txt}(output written to {browse  `"Table\SDU.tex"'})
{com}. {c )-}                                       
.                                         
. // table A7: Switching Vote From Establishment Left and Right to Populist Right, IV - RTI, German                                       
. {c -(}                                       
. eststo clear                                    
. eststo: qui logit switching2_broad rti  female age   foreign high i.rincome    [pw=phrf] if year==2018  , robust                                
{txt}({res}est1{txt} stored)
{com}. eststo: qui logit switching2_broad rti  female age   foreign high i.rincome offshwalt2  i.sampreg [pw=phrf] if year==2018       , robust                                
{txt}({res}est2{txt} stored)
{com}. eststo: qui logit switching2_broad rti  female age   foreign high i.rincome offshwalt2 relskillspec  i.sampreg [pw=phrf] if year==2018          , robust                        
{txt}({res}est3{txt} stored)
{com}. eststo: qui logit switching2_broad rti  female age   foreign high i.rincome offshwalt2 relskillspec t2 t3 i.sampreg [pw=phrf] if year==2018             , robust                        
{txt}({res}est4{txt} stored)
{com}. esttab , replace label se title(Switching Vote From Establishment Left and Right to Populist Right, IV - RTI, Germany \label {c -(}tab:Table1rtilongswnarrow2{c )-})mti("+Demographic" "+Offshoring" "+Skill"  "All") compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(rti  female age   foreign high offshwalt2 relskillspec t2 t3) scalars("N Observations" "r2_p R$^2$" "aic AIC") indicate( "Income = *income"  "Regional controls = *.sampreg")                                     
{res}
{txt}Switching Vote From Establishment Left and Right to Populist Right, IV - RTI, Germany \label {tab:Table1rtilongswnarrow2}
{txt}{hline 68}
{txt}                       (1)          (2)          (3)          (4)   
{txt}                 +Demogr~c    +Offsho~g       +Skill          All   
{txt}{hline 68}
{res}Switching Vote                                                      {txt}
{txt}RTI Index       {res}     0.124        0.209*       0.207*       0.269*  {txt}
                {res} {ralign 9:{txt:(}0.098{txt:)}}    {ralign 9:{txt:(}0.125{txt:)}}    {ralign 9:{txt:(}0.120{txt:)}}    {ralign 9:{txt:(}0.140{txt:)}}   {txt}
{txt}Female          {res}    -1.260***    -1.212***    -1.272***    -1.365***{txt}
                {res} {ralign 9:{txt:(}0.227{txt:)}}    {ralign 9:{txt:(}0.226{txt:)}}    {ralign 9:{txt:(}0.237{txt:)}}    {ralign 9:{txt:(}0.257{txt:)}}   {txt}
{txt}Age             {res}     0.003       -0.000        0.001        0.001   {txt}
                {res} {ralign 9:{txt:(}0.008{txt:)}}    {ralign 9:{txt:(}0.009{txt:)}}    {ralign 9:{txt:(}0.009{txt:)}}    {ralign 9:{txt:(}0.008{txt:)}}   {txt}
{txt}Foreign born    {res}    -1.125**     -0.966*      -0.948*      -0.973*  {txt}
                {res} {ralign 9:{txt:(}0.545{txt:)}}    {ralign 9:{txt:(}0.548{txt:)}}    {ralign 9:{txt:(}0.548{txt:)}}    {ralign 9:{txt:(}0.543{txt:)}}   {txt}
{txt}High-Skilled    {res}    -0.715**     -0.695**     -0.727**     -0.734** {txt}
                {res} {ralign 9:{txt:(}0.328{txt:)}}    {ralign 9:{txt:(}0.327{txt:)}}    {ralign 9:{txt:(}0.325{txt:)}}    {ralign 9:{txt:(}0.317{txt:)}}   {txt}
{txt}Offshorability  {res}                 -0.331       -0.278       -0.072   {txt}
                {res}              {ralign 9:{txt:(}0.270{txt:)}}    {ralign 9:{txt:(}0.277{txt:)}}    {ralign 9:{txt:(}0.322{txt:)}}   {txt}
{txt}Skill-Specific~y{res}                              -0.050       -0.056   {txt}
                {res}                           {ralign 9:{txt:(}0.035{txt:)}}    {ralign 9:{txt:(}0.038{txt:)}}   {txt}
{txt}Task-Tech       {res}                                            0.176   {txt}
                {res}                                        {ralign 9:{txt:(}0.347{txt:)}}   {txt}
{txt}Task-Inter      {res}                                            0.603   {txt}
                {res}                                        {ralign 9:{txt:(}0.412{txt:)}}   {txt}
{txt}Income          {res}       Yes          Yes          Yes          Yes   {txt}
{txt}Regional contr~s{res}        No          Yes          Yes          Yes   {txt}
{txt}{hline 68}
{txt}Observations    {res}      7522         7522         7522         7510   {txt}
{txt}R$^2$           {res}     0.068        0.081        0.084        0.087   {txt}
{txt}AIC             {res}   5.7e+06      5.6e+06      5.6e+06      5.6e+06   {txt}
{txt}{hline 68}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}. 
. esttab using "Table\SOEPlong_2.tex", replace label se title(Switching Vote From Establishment Left and Right to Populist Right, IV - RTI, Germany \label {c -(}tab:Table1rtilongswnarrow2{c )-})mti("+Demographic" "+Offshoring" "+Skill"  "All") compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(rti  female age   foreign high offshwalt2 relskillspec t2 t3) scalars("N Observations" "r2_p R$^2$" "aic AIC") indicate( "Income = *income"  "Regional controls = *.sampreg")                                 
{res}{txt}(output written to {browse  `"Table\SOEPlong_2.tex"'})
{com}. {c )-}                                       
.                                         
. // table A8: Switching Vote, IV - Routine (dummy), Germany                                      
. {c -(}                                       
.         eststo clear                            
. eststo: qui logit switching2_broad task3cog2and3  female age   foreign high i.rincome   [pw=phrf] if year==2018, robust                                 
{txt}({res}est1{txt} stored)
{com}.                                         
. eststo: qui logit switching2_broad task3cog2and3  female age   foreign high i.rincome offshwalt2  i.sampreg [pw=phrf] if year==2018             , robust                        
{txt}({res}est2{txt} stored)
{com}. eststo: qui logit switching2_broad task3cog2and3  female age   foreign high i.rincome offshwalt2 relskillspec i.sampreg [pw=phrf] if year==2018         , robust                        
{txt}({res}est3{txt} stored)
{com}.                                         
. eststo: qui logit switching2_broad task3cog2and3  female age   foreign high i.rincome offshwalt2 relskillspec t2 t3 i.sampreg [pw=phrf] if year==2018           , robust                        
{txt}({res}est4{txt} stored)
{com}. esttab , replace label se title(Switching Vote, IV - Routine (dummy), Germany \label {c -(}tab:Table1rtilongswnarrowtask3cog2and3{c )-})mti("+Demographic" "+Offshoring" "+Skill"  "All") compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(task3cog2and3  female age   foreign high offshwalt2 relskillspec t2 t3) scalars("N Observations" "r2_p R$^2$" "aic AIC") indicate( "Income = *.rincome"  "Regional controls = *.sampreg")                                     
{res}
{txt}Switching Vote, IV - Routine (dummy), Germany \label {tab:Table1rtilongswnarrowtask3cog2and3}
{txt}{hline 68}
{txt}                       (1)          (2)          (3)          (4)   
{txt}                 +Demogr~c    +Offsho~g       +Skill          All   
{txt}{hline 68}
{res}Switching Vote                                                      {txt}
{txt}Routine         {res}     0.472*       0.463*       0.525**      0.576** {txt}
                {res} {ralign 9:{txt:(}0.241{txt:)}}    {ralign 9:{txt:(}0.240{txt:)}}    {ralign 9:{txt:(}0.241{txt:)}}    {ralign 9:{txt:(}0.234{txt:)}}   {txt}
{txt}Female          {res}    -1.122***    -1.086***    -1.145***    -1.258***{txt}
                {res} {ralign 9:{txt:(}0.236{txt:)}}    {ralign 9:{txt:(}0.235{txt:)}}    {ralign 9:{txt:(}0.244{txt:)}}    {ralign 9:{txt:(}0.260{txt:)}}   {txt}
{txt}Age             {res}     0.002        0.001        0.001        0.001   {txt}
                {res} {ralign 9:{txt:(}0.008{txt:)}}    {ralign 9:{txt:(}0.008{txt:)}}    {ralign 9:{txt:(}0.008{txt:)}}    {ralign 9:{txt:(}0.008{txt:)}}   {txt}
{txt}Foreign born    {res}    -0.817       -0.664       -0.652       -0.668   {txt}
                {res} {ralign 9:{txt:(}0.504{txt:)}}    {ralign 9:{txt:(}0.504{txt:)}}    {ralign 9:{txt:(}0.504{txt:)}}    {ralign 9:{txt:(}0.499{txt:)}}   {txt}
{txt}High-Skilled    {res}    -0.712**     -0.724**     -0.752**     -0.784** {txt}
                {res} {ralign 9:{txt:(}0.310{txt:)}}    {ralign 9:{txt:(}0.313{txt:)}}    {ralign 9:{txt:(}0.312{txt:)}}    {ralign 9:{txt:(}0.305{txt:)}}   {txt}
{txt}Offshorability  {res}                 -0.083       -0.008        0.153   {txt}
                {res}              {ralign 9:{txt:(}0.217{txt:)}}    {ralign 9:{txt:(}0.227{txt:)}}    {ralign 9:{txt:(}0.322{txt:)}}   {txt}
{txt}Skill-Specific~y{res}                              -0.055       -0.047   {txt}
                {res}                           {ralign 9:{txt:(}0.033{txt:)}}    {ralign 9:{txt:(}0.034{txt:)}}   {txt}
{txt}Task-Tech       {res}                                           -0.086   {txt}
                {res}                                        {ralign 9:{txt:(}0.303{txt:)}}   {txt}
{txt}Task-Inter      {res}                                            0.334   {txt}
                {res}                                        {ralign 9:{txt:(}0.383{txt:)}}   {txt}
{txt}Income          {res}       Yes          Yes          Yes          Yes   {txt}
{txt}Regional contr~s{res}        No          Yes          Yes          Yes   {txt}
{txt}{hline 68}
{txt}Observations    {res}      8386         8386         8346         8334   {txt}
{txt}R$^2$           {res}     0.067        0.077        0.080        0.082   {txt}
{txt}AIC             {res}   6.1e+06      6.1e+06      6.0e+06      6.0e+06   {txt}
{txt}{hline 68}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}.                                         
.         esttab  using "Table\SOEPdummy_2.tex", replace label se title(Switching Vote, IV - Routine (dummy), Germany \label {c -(}tab:Table1rtilongswnarrowtask3cog2and3{c )-})mti("+Demographic" "+Offshoring" "+Skill"  "All") compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(task3cog2and3  female age   foreign high offshwalt2 relskillspec t2 t3) scalars("N Observations" "r2_p R$^2$" "aic AIC") indicate( "Income = *.rincome"  "Regional controls = *.sampreg")                               
{res}{txt}(output written to {browse  `"Table\SOEPdummy_2.tex"'})
{com}. {c )-}                                       
. {c )-}                                       
.                                         
. // table A9: Switching Vote (Only from the Right), IV - RTI                                     
.         eststo clear                            
.         eststo: qui logit switching2_r rti  female age   foreign high  i.rincome offshwalt2  i.sampreg [pw=phrf] if year==2018, robust                                                          
{txt}({res}est1{txt} stored)
{com}. eststo: qui logit switching2_r rti  female age   foreign high  i.rincome offshwalt2  i.sampreg [pw=phrf] if year==2018, robust  
{txt}({res}est2{txt} stored)
{com}. eststo: qui logit switching2_r rti  female age   foreign high  i.rincome offshwalt2 relskillspec  i.sampreg [pw=phrf] if year==2018     , robust                
{txt}({res}est3{txt} stored)
{com}. eststo: qui logit switching2_r rti  female age   foreign high  i.rincome offshwalt2 relskillspec t2 t3 i.sampreg [pw=phrf] if year==2018        , robust                                
{txt}({res}est4{txt} stored)
{com}.                 
. esttab , replace label se title(Switching Vote (Only from the Right), IV - RTI \label {c -(}tab:CDU{c )-})mti("+Demographic" "+Offshoring" "+Skill"  "All") compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(rti  female age   foreign high offshwalt2 relskillspec t2 t3) scalars( "N Observations" "r2_p R$^2$" "aic AIC" ) indicate( "Income = *income"  "Regional controls = *.sampreg")                                     
{res}
{txt}Switching Vote (Only from the Right), IV - RTI \label {tab:CDU}
{txt}{hline 68}
{txt}                       (1)          (2)          (3)          (4)   
{txt}                 +Demogr~c    +Offsho~g       +Skill          All   
{txt}{hline 68}
{res}Right to AfD                                                        {txt}
{txt}RTI Index       {res}    -0.251       -0.251       -0.244       -0.113   {txt}
                {res} {ralign 9:{txt:(}0.335{txt:)}}    {ralign 9:{txt:(}0.335{txt:)}}    {ralign 9:{txt:(}0.307{txt:)}}    {ralign 9:{txt:(}0.324{txt:)}}   {txt}
{txt}Female          {res}    -1.781***    -1.781***    -1.854***    -2.097***{txt}
                {res} {ralign 9:{txt:(}0.650{txt:)}}    {ralign 9:{txt:(}0.650{txt:)}}    {ralign 9:{txt:(}0.654{txt:)}}    {ralign 9:{txt:(}0.688{txt:)}}   {txt}
{txt}Age             {res}    -0.001       -0.001       -0.000        0.002   {txt}
                {res} {ralign 9:{txt:(}0.029{txt:)}}    {ralign 9:{txt:(}0.029{txt:)}}    {ralign 9:{txt:(}0.029{txt:)}}    {ralign 9:{txt:(}0.030{txt:)}}   {txt}
{txt}Foreign born    {res}     0.797        0.797        0.671        0.522   {txt}
                {res} {ralign 9:{txt:(}1.051{txt:)}}    {ralign 9:{txt:(}1.051{txt:)}}    {ralign 9:{txt:(}1.072{txt:)}}    {ralign 9:{txt:(}1.093{txt:)}}   {txt}
{txt}High-Skilled    {res}    -0.406       -0.406       -0.461       -0.713   {txt}
                {res} {ralign 9:{txt:(}0.806{txt:)}}    {ralign 9:{txt:(}0.806{txt:)}}    {ralign 9:{txt:(}0.806{txt:)}}    {ralign 9:{txt:(}0.560{txt:)}}   {txt}
{txt}Offshorability  {res}     1.140*       1.140*       1.241*       2.050** {txt}
                {res} {ralign 9:{txt:(}0.668{txt:)}}    {ralign 9:{txt:(}0.668{txt:)}}    {ralign 9:{txt:(}0.691{txt:)}}    {ralign 9:{txt:(}1.008{txt:)}}   {txt}
{txt}Skill-Specific~y{res}                              -0.113       -0.074   {txt}
                {res}                           {ralign 9:{txt:(}0.094{txt:)}}    {ralign 9:{txt:(}0.095{txt:)}}   {txt}
{txt}Task-Tech       {res}                                           -0.217   {txt}
                {res}                                        {ralign 9:{txt:(}1.092{txt:)}}   {txt}
{txt}Task-Inter      {res}                                            1.517   {txt}
                {res}                                        {ralign 9:{txt:(}1.203{txt:)}}   {txt}
{txt}Income          {res}       Yes          Yes          Yes          Yes   {txt}
{txt}Regional contr~s{res}       Yes          Yes          Yes          Yes   {txt}
{txt}{hline 68}
{txt}Observations    {res}      1484         1484         1484         1478   {txt}
{txt}R$^2$           {res}     0.171        0.171        0.179        0.208   {txt}
{txt}AIC             {res}   7.3e+05      7.3e+05      7.3e+05      7.0e+05   {txt}
{txt}{hline 68}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}.                                         
. esttab using "Table\CDU.tex", replace label se title(Switching Vote (Only from the Right), IV - RTI, Germany \label {c -(}tab:CDU{c )-})mti("+Demographic" "+Offshoring" "+Skill"  "All")  compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(rti  female age   foreign high offshwalt2 relskillspec t2 t3) scalars( "N Observations" "r2_p R$^2$" "aic AIC" )indicate( "Income = *income"  "Regional controls = *.sampreg")                                       
{res}{txt}(output written to {browse  `"Table\CDU.tex"'})
{com}.                                         
.                                         
. {c )-}                                       
. {c )-}                                       
{txt}
{com}. *##########################################                                     
. * Descriptive                                   
. *##########################################                                     
. {c -(}                                       
. // table A2: Descriptive statistic: Germany SOEP 2014 vs 2018.                                  
. {c -(}                       
.                 eststo clear
.                 
. qui estpost sum switching2_broad   rti age income female  foreign unemployed high  offshwalt2 relskillspec t2 t3 sampreg [w=phrf] if switching2_broad~=., d                                     
.                                         
. esttab  ,  /// ,  ,                                     
>         cells("mean(label(Mean) fmt(2)) p50(label(Median) fmt(2)) sd(label(S.D.) fmt(2)) min(label(Min.) fmt(0)) max(label(Max) fmt(0)) count(label(Obs.) fmt(0))") ///                         
>         nonumber label replace noobs varlabels(distance_redist "Distance Redistribution" distance_div "Distance Diversity" distance_fixed "Distance Fixed Attributes" PRITM "PR with Trichotomous Multipartism" totseats "Total Number of Seats" number2 "Total Number of Parties" oecdmember "OECD member") nomtitle   
{res}
{txt}{hline 98}
{txt}                             Mean       Median         S.D.         Min.          Max         Obs.
{txt}{hline 98}
{txt}Switching Vote      {res}         0.03         0.00         0.16            0            1        29235{txt}
{txt}RTI Index           {res}        -0.12        -0.44         0.93           -2            2        10185{txt}
{txt}Age                 {res}        50.75        51.00        18.91           17          103        29235{txt}
{txt}income              {res}      2715.32      2500.00      1879.23           20        40000        13712{txt}
{txt}Female              {res}         0.51         1.00         0.50            0            1        29235{txt}
{txt}Foreign born        {res}         0.12         0.00         0.33            0            1        29235{txt}
{txt}Unemployed          {res}         0.04         0.00         0.20            0            1        29207{txt}
{txt}High-Skilled        {res}         0.32         0.00         0.47            0            1        29235{txt}
{txt}Offshorability      {res}         0.46         0.00         0.50            0            1        11383{txt}
{txt}Skill-Specificity   {res}         4.23         3.58         3.26            1           25        11333{txt}
{txt}Task-Tech           {res}         0.34         0.00         0.47            0            1        11319{txt}
{txt}Task-Inter          {res}         0.37         0.00         0.48            0            1        11319{txt}
{txt}Region (West 1 - ~2){res}         1.17         1.00         0.38            1            2        29235{txt}
{txt}{hline 98}
{com}.         
. esttab using "Table\summarystats_Germany.tex" ,  /// ,  ,                                       
>         cells("mean(label(Mean) fmt(2)) p50(label(Median) fmt(2)) sd(label(S.D.) fmt(2)) min(label(Min.) fmt(0)) max(label(Max) fmt(0)) count(label(Obs.) fmt(0))") ///                         
>         nonumber label replace noobs varlabels(distance_redist "Distance Redistribution" distance_div "Distance Diversity" distance_fixed "Distance Fixed Attributes" PRITM "PR with Trichotomous Multipartism" totseats "Total Number of Seats" number2 "Total Number of Parties" oecdmember "OECD member") nomtitle                           
{res}{txt}(output written to {browse  `"Table\summarystats_Germany.tex"'})
{com}. {c )-}                                       
. {c )-}                                       
{txt}
{com}. 
{txt}end of do-file

{com}. do "./Do/1_3_Switching_Appendix_SpoonKluver.do"
{txt}
{com}. *****************************************************************************
. *    Table Additional Context Spoon & Kluever's replication data                *
. *                                                                                                                                                       *                       
. * Author:                       Valentina Gonzalez Rostani                                                      *
. * Contact:                      mag384@pitt.edu                                                                 *
. * Date:                         August 9 2024                                                                                   *
. * Version:                      Stata 17                                                                                                *                                                                          
. *                                                                                                                                                       *
. *****************************************************************************
. /*
> This do-file:
> - Creates Table A10 using data from Spoon & Kluever's replication data (EJPR, 2019). 
> 
> Input:
> - Data\SpoonKluever_2019_EJPR_PartyConvergence.dta
> 
> Output:
> - Table A10: Switching in Germany from mainstream to non-mainstream parties 2002-2013
> 
> */
. 
. *Defining Directory
. cd "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication"
{res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication
{txt}
{com}. 
. *##########################################
. * Load data
. *##########################################
. {c -(}
. *Calling the data
. use "Data\SpoonKluever_2019_EJPR_PartyConvergence.dta", clear 
{txt}(Spoon/Klüver (2019) EJPR: Party covergence and vote switching)
{com}. {c )-}
{txt}
{com}. *******************************************************************************
. * Preparing variables
. *******************************************************************************
. {c -(}
. * Election years
. gen year = 2002 in 1
{txt}(14,633 missing values generated)
{com}. replace year = 2005 in 2
{txt}(1 real change made)
{com}. replace year = 2009 in 3
{txt}(1 real change made)
{com}. replace year = 2013 in 4
{txt}(1 real change made)
{com}. 
. * Initialize variables
. gen SPD = .
{txt}(14,634 missing values generated)
{com}. gen Liberal = .
{txt}(14,634 missing values generated)
{com}. gen CDU = .
{txt}(14,634 missing values generated)
{com}. gen Total_Switching = .
{txt}(14,634 missing values generated)
{com}. 
. * Election dates and party codes
. local dates "22sep2002 18sep2005 27sep2009 22sep2013"
. local SPD 41320
. local Liberal 41420
. local CDU 41521
. 
. * Loop over each date
. local i = 1
. foreach date of local dates {c -(}
{txt}  2{com}.     * Total Switching for the date
.     quietly summarize switch_main if country == 41 & edate == date("`date'", "DMY")
{txt}  3{com}.     replace Total_Switching = r(mean) * 100 in `i'
{txt}  4{com}. 
.     * SPD
.     quietly summarize switch_main if country == 41 & edate == date("`date'", "DMY") & party_last == `SPD'
{txt}  5{com}.     replace SPD = r(mean) * 100 in `i'
{txt}  6{com}. 
.     * Liberal
.     quietly summarize switch_main if country == 41 & edate == date("`date'", "DMY") & party_last == `Liberal'
{txt}  7{com}.     replace Liberal = r(mean) * 100 in `i'
{txt}  8{com}. 
.     * CDU
.     quietly summarize switch_main if country == 41 & edate == date("`date'", "DMY") & party_last == `CDU'
{txt}  9{com}.     replace CDU = r(mean) * 100 in `i'
{txt} 10{com}. 
.     local i = `i' + 1
{txt} 11{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
{com}. {c )-}
{txt}
{com}. // table A10: Switching in Germany from mainstream to non-mainstream parties 2002-2009
. {c -(}
. * Format the variables to display one decimal place
. format SPD Liberal CDU Total_Switching %4.1f
. 
. 
. * Use tabdisp to display the results in a tabular format
. tabdisp year, c(SPD Liberal CDU Total_Switching)

{txt}{hline 10}{c TT}{hline 67}
     year {c |}             SPD          Liberal              CDU  Total_Switching
{hline 10}{c +}{hline 67}
     2002 {c |}            {res}10.6             12.0              1.5              7.1
     {txt}2005 {c |}             {res}0.4              8.1              4.1              5.0
     {txt}2009 {c |}             {res}1.9              9.1              0.9              3.8
     {txt}2013 {c |}             {res}9.8             22.2              3.1              6.7
        {txt}. {c |}                {res}                                                   
{txt}{hline 10}{c BT}{hline 67}
{com}. {c )-}
{txt}
{com}. 
{txt}end of do-file

{com}. do "./Do/1_4_Switching_Appendix_ESS.do"
{txt}
{com}. *****************************************************************************
. *                                 Figures Descriptives with ESS                                         *
. *                                                                                                                                                       *                       
. * Author:                       Valentina Gonzalez Rostani                                                      *
. * Contact:                      mag384@pitt.edu                                                                 *
. * Date:                         August 9 2024                                                                                   *
. * Version:                      Stata 17                                                                                                *                                                                          
. *                                                                                                                                                       *
. *****************************************************************************
. /*
> This do-file:
> - Creates Table A11 using data from the ESS. 
> 
> Input:
> - Data\Appendix_ESS.dta
> 
> Output:
> - Table A11: Switching in Western European Countries from Mainstream Left to Outsider Radical Right parties 2002-2018 [Table\ESS_switching.tex]
> 
> */
. 
. *Defining Directory
. cd "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication"
{res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication
{txt}
{com}. 
. *##########################################
. * Load data
. *##########################################
. use "Data\Appendix_ESS.dta", clear 
{txt}
{com}. 
. *****************************************************************************
. * Preparing variables
. *****************************************************************************
. {c -(}
. // Generate a binary variable 'west2' to identify whether a country belongs to the specified list of Western countries. 
. gen west2=(cou=="BEL" | cou=="DNK" | cou=="FIN" | cou=="FRA" | cou=="DEU" | cou=="GRC" | cou=="IRL" | cou=="NLD" | cou=="PRT" | cou=="ESP" | cou=="SWE" | cou=="GBR" | cou=="NOR" | cou=="CHE" | cou=="AUT" | cou=="ITA" | cou=="POL" )
. 
. // Generates a variable called switching comparing parfam and parfam_close. 
.         * Parfam indicates the party family of the  party voted in the last election. 
.         * parfam_close indicates the party family of the party the subject feel close now. 
.         * Switching is defined as voted for another party but now feel close to a nationalist. 
. gen switching2=.
{txt}(424,645 missing values generated)
{com}. replace switching2 =0 if parfam==parfam_close & parfam_close~=. & parfam~=.
{txt}(264,683 real changes made)
{com}. replace switching2 =0 if parfam~=parfam_close & parfam_close~=70 
{txt}(150,503 real changes made)
{com}. replace switching2 =1 if parfam~=70 & parfam_close==70 
{txt}(3,723 real changes made)
{com}. 
. // Generates a variable called switching comparing parfam and parfam_close. The difference with the one before is that this one just look at the left instead of any party.  
. gen switching2_leftboth=.
{txt}(424,645 missing values generated)
{com}. replace switching2_leftboth =0 if parfam==parfam_close & parfam_close~=. & parfam~=.
{txt}(264,683 real changes made)
{com}. replace switching2_leftboth =0 if parfam~=parfam_close & parfam_close~=70 
{txt}(150,503 real changes made)
{com}. replace switching2_leftboth =1 if (parfam==30 | parfam==20 ) & (parfam_close==70 ) 
{txt}(301 real changes made)
{com}. 
. 
. // Encode the string variable 'cntry' into a numeric variable 'country2' with value labels
. encode cntry, gen(country2)
. 
. // Create a new variable 'countr_year' by multiplying 'country2' by 'year'. This creates a unique identifier for each country-year combination
. gen countr_year=country2*year
{txt}(1,651 missing values generated)
{com}. 
. {c )-}
{txt}
{com}. 
. // table A11: Switching in Western European Countries from Mainstream Left to Outsider Radical Right parties 2002-2018
. {c -(}
. eststo clear  // Clear any previously stored estimates
. 
. // Estimate logistic regression for 'switching2' with various predictors, storing the results. First results are swithing from any party. 
. eststo:  logit switching2  meanprobfreyosborne  unemplindiv2 female agea mbtru2 rlgdgr    i.country2 i.year  if west2==1, cluster(countr_year)

{txt}note: {bf:11.country2} != 0 predicts failure perfectly;
      {bf:11.country2} omitted and 10003 obs not used.

note: {bf:14.country2} != 0 predicts failure perfectly;
      {bf:14.country2} omitted and 14459 obs not used.

note: {bf:18.country2} != 0 predicts failure perfectly;
      {bf:18.country2} omitted and 14384 obs not used.

note: {bf:28.country2} != 0 predicts failure perfectly;
      {bf:28.country2} omitted and 11095 obs not used.

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-10332.283}  
Iteration 1:{space 3}log pseudolikelihood = {res:-9792.0025}  
Iteration 2:{space 3}log pseudolikelihood = {res:-9518.1894}  
Iteration 3:{space 3}log pseudolikelihood = {res:-9509.0329}  
Iteration 4:{space 3}log pseudolikelihood = {res:-9508.4857}  
Iteration 5:{space 3}log pseudolikelihood = {res:-9508.4842}  
Iteration 6:{space 3}log pseudolikelihood = {res:-9508.4842}  
{res}
{txt}Logistic regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:148,688}
{txt}{col 56}{lalign 13:Wald chi2({res:26})}{col 69} = {res}{ralign 7:832.43}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}Log pseudolikelihood = {res:-9508.4842}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.0797}

{txt}{ralign 85:(Std. err. adjusted for {res:102} clusters in countr_year)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}         switching2{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      z{col 53}   P>|z|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
meanprobfreyosborne {c |}{col 21}{res}{space 2}  .626453{col 33}{space 2} .0774689{col 44}{space 1}    8.09{col 53}{space 3}0.000{col 61}{space 4} .4746167{col 74}{space 3} .7782894
{txt}{space 7}unemplindiv2 {c |}{col 21}{res}{space 2} .3177039{col 33}{space 2} .1060878{col 44}{space 1}    2.99{col 53}{space 3}0.003{col 61}{space 4} .1097756{col 74}{space 3} .5256321
{txt}{space 13}female {c |}{col 21}{res}{space 2}-.4446256{col 33}{space 2} .0489859{col 44}{space 1}   -9.08{col 53}{space 3}0.000{col 61}{space 4}-.5406361{col 74}{space 3} -.348615
{txt}{space 15}agea {c |}{col 21}{res}{space 2}-.0124541{col 33}{space 2} .0016757{col 44}{space 1}   -7.43{col 53}{space 3}0.000{col 61}{space 4}-.0157385{col 74}{space 3}-.0091698
{txt}{space 13}mbtru2 {c |}{col 21}{res}{space 2} .0094365{col 33}{space 2} .0629853{col 44}{space 1}    0.15{col 53}{space 3}0.881{col 61}{space 4}-.1140124{col 74}{space 3} .1328855
{txt}{space 13}rlgdgr {c |}{col 21}{res}{space 2}-.0180493{col 33}{space 2} .0092033{col 44}{space 1}   -1.96{col 53}{space 3}0.050{col 61}{space 4}-.0360875{col 74}{space 3}-.0000111
{txt}{space 19} {c |}
{space 11}country2 {c |}
{space 16}BE  {c |}{col 21}{res}{space 2}-.4001553{col 33}{space 2} .2427938{col 44}{space 1}   -1.65{col 53}{space 3}0.099{col 61}{space 4}-.8760225{col 74}{space 3} .0757118
{txt}{space 16}CH  {c |}{col 21}{res}{space 2}-2.077773{col 33}{space 2} .2296245{col 44}{space 1}   -9.05{col 53}{space 3}0.000{col 61}{space 4}-2.527829{col 74}{space 3}-1.627717
{txt}{space 16}DE  {c |}{col 21}{res}{space 2}-.8326856{col 33}{space 2} .4874099{col 44}{space 1}   -1.71{col 53}{space 3}0.088{col 61}{space 4}-1.787991{col 74}{space 3} .1226202
{txt}{space 16}DK  {c |}{col 21}{res}{space 2} .5732042{col 33}{space 2} .1330151{col 44}{space 1}    4.31{col 53}{space 3}0.000{col 61}{space 4} .3124994{col 74}{space 3} .8339091
{txt}{space 16}ES  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}FI  {c |}{col 21}{res}{space 2} .3173617{col 33}{space 2} .2207106{col 44}{space 1}    1.44{col 53}{space 3}0.150{col 61}{space 4} -.115223{col 74}{space 3} .7499465
{txt}{space 16}FR  {c |}{col 21}{res}{space 2} .5748686{col 33}{space 2} .1522983{col 44}{space 1}    3.77{col 53}{space 3}0.000{col 61}{space 4} .2763695{col 74}{space 3} .8733677
{txt}{space 16}GB  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}GR  {c |}{col 21}{res}{space 2}-1.380513{col 33}{space 2} .3591819{col 44}{space 1}   -3.84{col 53}{space 3}0.000{col 61}{space 4}-2.084497{col 74}{space 3}-.6765299
{txt}{space 16}IE  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}IT  {c |}{col 21}{res}{space 2} .2244332{col 33}{space 2} .3575719{col 44}{space 1}    0.63{col 53}{space 3}0.530{col 61}{space 4}-.4763948{col 74}{space 3} .9252611
{txt}{space 16}NL  {c |}{col 21}{res}{space 2} .6197002{col 33}{space 2} .1056598{col 44}{space 1}    5.87{col 53}{space 3}0.000{col 61}{space 4} .4126108{col 74}{space 3} .8267895
{txt}{space 16}NO  {c |}{col 21}{res}{space 2} 1.028404{col 33}{space 2} .2938573{col 44}{space 1}    3.50{col 53}{space 3}0.000{col 61}{space 4} .4524547{col 74}{space 3} 1.604354
{txt}{space 16}PL  {c |}{col 21}{res}{space 2}-3.227171{col 33}{space 2} .6909371{col 44}{space 1}   -4.67{col 53}{space 3}0.000{col 61}{space 4}-4.581382{col 74}{space 3}-1.872959
{txt}{space 16}PT  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}SE  {c |}{col 21}{res}{space 2}-.5506039{col 33}{space 2} .3257236{col 44}{space 1}   -1.69{col 53}{space 3}0.091{col 61}{space 4} -1.18901{col 74}{space 3} .0878025
{txt}{space 19} {c |}
{space 15}year {c |}
{space 14}2004  {c |}{col 21}{res}{space 2}-.0077375{col 33}{space 2} .4212825{col 44}{space 1}   -0.02{col 53}{space 3}0.985{col 61}{space 4}-.8334359{col 74}{space 3}  .817961
{txt}{space 14}2006  {c |}{col 21}{res}{space 2}-.1070727{col 33}{space 2}  .423179{col 44}{space 1}   -0.25{col 53}{space 3}0.800{col 61}{space 4}-.9364882{col 74}{space 3} .7223428
{txt}{space 14}2008  {c |}{col 21}{res}{space 2} .3506005{col 33}{space 2} .3490065{col 44}{space 1}    1.00{col 53}{space 3}0.315{col 61}{space 4}-.3334397{col 74}{space 3} 1.034641
{txt}{space 14}2010  {c |}{col 21}{res}{space 2} .5733898{col 33}{space 2} .3671428{col 44}{space 1}    1.56{col 53}{space 3}0.118{col 61}{space 4}-.1461969{col 74}{space 3} 1.292976
{txt}{space 14}2012  {c |}{col 21}{res}{space 2}  .334377{col 33}{space 2} .3149788{col 44}{space 1}    1.06{col 53}{space 3}0.288{col 61}{space 4}  -.28297{col 74}{space 3} .9517241
{txt}{space 14}2014  {c |}{col 21}{res}{space 2} .8281829{col 33}{space 2}  .335539{col 44}{space 1}    2.47{col 53}{space 3}0.014{col 61}{space 4} .1705386{col 74}{space 3} 1.485827
{txt}{space 14}2016  {c |}{col 21}{res}{space 2} .9166708{col 33}{space 2} .3427002{col 44}{space 1}    2.67{col 53}{space 3}0.007{col 61}{space 4} .2449908{col 74}{space 3} 1.588351
{txt}{space 14}2018  {c |}{col 21}{res}{space 2} .7136866{col 33}{space 2} .3243732{col 44}{space 1}    2.20{col 53}{space 3}0.028{col 61}{space 4} .0779268{col 74}{space 3} 1.349446
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-4.336217{col 33}{space 2} .3048835{col 44}{space 1}  -14.22{col 53}{space 3}0.000{col 61}{space 4}-4.933778{col 74}{space 3}-3.738657
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)
{com}. eststo:  logit switching2  rti   unemplindiv2  female agea mbtru2 rlgdgr     i.country2 i.year if west2==1, cluster(countr_year)

{txt}note: {bf:11.country2} != 0 predicts failure perfectly;
      {bf:11.country2} omitted and 9697 obs not used.

note: {bf:14.country2} != 0 predicts failure perfectly;
      {bf:14.country2} omitted and 16770 obs not used.

note: {bf:18.country2} != 0 predicts failure perfectly;
      {bf:18.country2} omitted and 15405 obs not used.

note: {bf:28.country2} != 0 predicts failure perfectly;
      {bf:28.country2} omitted and 10977 obs not used.

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-10775.814}  
Iteration 1:{space 3}log pseudolikelihood = {res:-10228.172}  
Iteration 2:{space 3}log pseudolikelihood = {res:-9962.5478}  
Iteration 3:{space 3}log pseudolikelihood = {res:-9952.8577}  
Iteration 4:{space 3}log pseudolikelihood = {res:-9952.1262}  
Iteration 5:{space 3}log pseudolikelihood = {res:-9952.1193}  
Iteration 6:{space 3}log pseudolikelihood = {res:-9952.1193}  
{res}
{txt}Logistic regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:153,158}
{txt}{col 56}{lalign 13:Wald chi2({res:26})}{col 69} = {res}{ralign 7:700.45}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}Log pseudolikelihood = {res:-9952.1193}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.0764}

{txt}{ralign 78:(Std. err. adjusted for {res:102} clusters in countr_year)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  switching2{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rti {c |}{col 14}{res}{space 2} .0441593{col 26}{space 2} .0244211{col 37}{space 1}    1.81{col 46}{space 3}0.071{col 54}{space 4}-.0037051{col 67}{space 3} .0920237
{txt}unemplindiv2 {c |}{col 14}{res}{space 2} .3122912{col 26}{space 2} .1055502{col 37}{space 1}    2.96{col 46}{space 3}0.003{col 54}{space 4} .1054167{col 67}{space 3} .5191657
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.4169935{col 26}{space 2} .0506936{col 37}{space 1}   -8.23{col 46}{space 3}0.000{col 54}{space 4}-.5163511{col 67}{space 3}-.3176359
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0126036{col 26}{space 2} .0016522{col 37}{space 1}   -7.63{col 46}{space 3}0.000{col 54}{space 4}-.0158418{col 67}{space 3}-.0093653
{txt}{space 6}mbtru2 {c |}{col 14}{res}{space 2}-.0152071{col 26}{space 2} .0596938{col 37}{space 1}   -0.25{col 46}{space 3}0.799{col 54}{space 4}-.1322049{col 67}{space 3} .1017906
{txt}{space 6}rlgdgr {c |}{col 14}{res}{space 2}-.0132213{col 26}{space 2} .0090904{col 37}{space 1}   -1.45{col 46}{space 3}0.146{col 54}{space 4} -.031038{col 67}{space 3} .0045955
{txt}{space 12} {c |}
{space 4}country2 {c |}
{space 9}BE  {c |}{col 14}{res}{space 2} -.398447{col 26}{space 2} .2230251{col 37}{space 1}   -1.79{col 46}{space 3}0.074{col 54}{space 4}-.8355681{col 67}{space 3} .0386741
{txt}{space 9}CH  {c |}{col 14}{res}{space 2}-2.021807{col 26}{space 2} .2334165{col 37}{space 1}   -8.66{col 46}{space 3}0.000{col 54}{space 4}-2.479295{col 67}{space 3}-1.564319
{txt}{space 9}DE  {c |}{col 14}{res}{space 2}-.9344977{col 26}{space 2} .4922578{col 37}{space 1}   -1.90{col 46}{space 3}0.058{col 54}{space 4}-1.899305{col 67}{space 3} .0303098
{txt}{space 9}DK  {c |}{col 14}{res}{space 2} .6101159{col 26}{space 2} .1303739{col 37}{space 1}    4.68{col 46}{space 3}0.000{col 54}{space 4} .3545877{col 67}{space 3} .8656442
{txt}{space 9}ES  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}FI  {c |}{col 14}{res}{space 2} .3239792{col 26}{space 2} .2341196{col 37}{space 1}    1.38{col 46}{space 3}0.166{col 54}{space 4}-.1348868{col 67}{space 3} .7828451
{txt}{space 9}FR  {c |}{col 14}{res}{space 2} .5489686{col 26}{space 2} .1577716{col 37}{space 1}    3.48{col 46}{space 3}0.001{col 54}{space 4} .2397418{col 67}{space 3} .8581953
{txt}{space 9}GB  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}GR  {c |}{col 14}{res}{space 2}-1.416238{col 26}{space 2} .3483663{col 37}{space 1}   -4.07{col 46}{space 3}0.000{col 54}{space 4}-2.099023{col 67}{space 3}-.7334526
{txt}{space 9}IE  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}IT  {c |}{col 14}{res}{space 2} .2270808{col 26}{space 2} .3714044{col 37}{space 1}    0.61{col 46}{space 3}0.541{col 54}{space 4}-.5008585{col 67}{space 3}   .95502
{txt}{space 9}NL  {c |}{col 14}{res}{space 2} .5277525{col 26}{space 2} .1080579{col 37}{space 1}    4.88{col 46}{space 3}0.000{col 54}{space 4}  .315963{col 67}{space 3}  .739542
{txt}{space 9}NO  {c |}{col 14}{res}{space 2} 1.000118{col 26}{space 2} .2904519{col 37}{space 1}    3.44{col 46}{space 3}0.001{col 54}{space 4} .4308429{col 67}{space 3} 1.569393
{txt}{space 9}PL  {c |}{col 14}{res}{space 2}   -3.601{col 26}{space 2} .6843002{col 37}{space 1}   -5.26{col 46}{space 3}0.000{col 54}{space 4}-4.942204{col 67}{space 3}-2.259796
{txt}{space 9}PT  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}SE  {c |}{col 14}{res}{space 2}-.6319244{col 26}{space 2} .3478986{col 37}{space 1}   -1.82{col 46}{space 3}0.069{col 54}{space 4}-1.313793{col 67}{space 3} .0499443
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2004  {c |}{col 14}{res}{space 2}-.1043655{col 26}{space 2} .4163638{col 37}{space 1}   -0.25{col 46}{space 3}0.802{col 54}{space 4}-.9204234{col 67}{space 3} .7116925
{txt}{space 7}2006  {c |}{col 14}{res}{space 2}-.1496606{col 26}{space 2} .4151763{col 37}{space 1}   -0.36{col 46}{space 3}0.718{col 54}{space 4}-.9633911{col 67}{space 3} .6640699
{txt}{space 7}2008  {c |}{col 14}{res}{space 2} .2917426{col 26}{space 2}  .332932{col 37}{space 1}    0.88{col 46}{space 3}0.381{col 54}{space 4}-.3607922{col 67}{space 3} .9442773
{txt}{space 7}2010  {c |}{col 14}{res}{space 2} .4673401{col 26}{space 2} .3602084{col 37}{space 1}    1.30{col 46}{space 3}0.194{col 54}{space 4}-.2386553{col 67}{space 3} 1.173336
{txt}{space 7}2012  {c |}{col 14}{res}{space 2} .2897848{col 26}{space 2} .3087396{col 37}{space 1}    0.94{col 46}{space 3}0.348{col 54}{space 4}-.3153337{col 67}{space 3} .8949033
{txt}{space 7}2014  {c |}{col 14}{res}{space 2} .7882495{col 26}{space 2}  .326437{col 37}{space 1}    2.41{col 46}{space 3}0.016{col 54}{space 4} .1484448{col 67}{space 3} 1.428054
{txt}{space 7}2016  {c |}{col 14}{res}{space 2} .8735676{col 26}{space 2} .3370411{col 37}{space 1}    2.59{col 46}{space 3}0.010{col 54}{space 4} .2129791{col 67}{space 3} 1.534156
{txt}{space 7}2018  {c |}{col 14}{res}{space 2} .6268198{col 26}{space 2} .3342719{col 37}{space 1}    1.88{col 46}{space 3}0.061{col 54}{space 4}-.0283411{col 67}{space 3} 1.281981
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-3.910465{col 26}{space 2} .2967508{col 37}{space 1}  -13.18{col 46}{space 3}0.000{col 54}{space 4}-4.492086{col 67}{space 3}-3.328844
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)
{com}. eststo:  logit switching2  task3cog2and3   unemplindiv2  female agea mbtru2 rlgdgr i.country2 i.year  if west2==1, cluster(countr_year)

{txt}note: {bf:11.country2} != 0 predicts failure perfectly;
      {bf:11.country2} omitted and 11028 obs not used.

note: {bf:14.country2} != 0 predicts failure perfectly;
      {bf:14.country2} omitted and 18354 obs not used.

note: {bf:18.country2} != 0 predicts failure perfectly;
      {bf:18.country2} omitted and 17616 obs not used.

note: {bf:28.country2} != 0 predicts failure perfectly;
      {bf:28.country2} omitted and 12485 obs not used.

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-11674.141}  
Iteration 1:{space 3}log pseudolikelihood = {res:-11016.757}  
Iteration 2:{space 3}log pseudolikelihood = {res:-10675.189}  
Iteration 3:{space 3}log pseudolikelihood = {res:-10664.585}  
Iteration 4:{space 3}log pseudolikelihood = {res:-10663.867}  
Iteration 5:{space 3}log pseudolikelihood = {res:-10663.863}  
Iteration 6:{space 3}log pseudolikelihood = {res:-10663.863}  
{res}
{txt}Logistic regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:174,298}
{txt}{col 56}{lalign 13:Wald chi2({res:26})}{col 69} = {res}{ralign 7:931.90}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}Log pseudolikelihood = {res:-10663.863}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.0865}

{txt}{ralign 79:(Std. err. adjusted for {res:102} clusters in countr_year)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}   switching2{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
task3cog2and3 {c |}{col 15}{res}{space 2} .6982428{col 27}{space 2} .0563492{col 38}{space 1}   12.39{col 47}{space 3}0.000{col 55}{space 4} .5878004{col 68}{space 3} .8086852
{txt}{space 1}unemplindiv2 {c |}{col 15}{res}{space 2} .2698213{col 27}{space 2} .1026312{col 38}{space 1}    2.63{col 47}{space 3}0.009{col 55}{space 4} .0686677{col 68}{space 3} .4709748
{txt}{space 7}female {c |}{col 15}{res}{space 2}-.4637686{col 27}{space 2}  .047182{col 38}{space 1}   -9.83{col 47}{space 3}0.000{col 55}{space 4}-.5562436{col 68}{space 3}-.3712936
{txt}{space 9}agea {c |}{col 15}{res}{space 2}-.0116815{col 27}{space 2} .0015433{col 38}{space 1}   -7.57{col 47}{space 3}0.000{col 55}{space 4}-.0147064{col 68}{space 3}-.0086567
{txt}{space 7}mbtru2 {c |}{col 15}{res}{space 2}-.0036168{col 27}{space 2} .0591219{col 38}{space 1}   -0.06{col 47}{space 3}0.951{col 55}{space 4}-.1194936{col 68}{space 3}   .11226
{txt}{space 7}rlgdgr {c |}{col 15}{res}{space 2}-.0204562{col 27}{space 2} .0086958{col 38}{space 1}   -2.35{col 47}{space 3}0.019{col 55}{space 4}-.0374997{col 68}{space 3}-.0034128
{txt}{space 13} {c |}
{space 5}country2 {c |}
{space 10}BE  {c |}{col 15}{res}{space 2}-.3584645{col 27}{space 2} .2311338{col 38}{space 1}   -1.55{col 47}{space 3}0.121{col 55}{space 4}-.8114784{col 68}{space 3} .0945495
{txt}{space 10}CH  {c |}{col 15}{res}{space 2} -1.89015{col 27}{space 2} .2259069{col 38}{space 1}   -8.37{col 47}{space 3}0.000{col 55}{space 4}-2.332919{col 68}{space 3} -1.44738
{txt}{space 10}DE  {c |}{col 15}{res}{space 2}-.8837012{col 27}{space 2} .4948342{col 38}{space 1}   -1.79{col 47}{space 3}0.074{col 55}{space 4}-1.853558{col 68}{space 3}  .086156
{txt}{space 10}DK  {c |}{col 15}{res}{space 2} .6262212{col 27}{space 2} .1282159{col 38}{space 1}    4.88{col 47}{space 3}0.000{col 55}{space 4} .3749226{col 68}{space 3} .8775198
{txt}{space 10}ES  {c |}{col 15}{res}{space 2}        0{col 27}{txt}  (empty)
{space 10}FI  {c |}{col 15}{res}{space 2} .3468653{col 27}{space 2} .2228829{col 38}{space 1}    1.56{col 47}{space 3}0.120{col 55}{space 4}-.0899771{col 68}{space 3} .7837076
{txt}{space 10}FR  {c |}{col 15}{res}{space 2} .5881999{col 27}{space 2} .1426971{col 38}{space 1}    4.12{col 47}{space 3}0.000{col 55}{space 4} .3085187{col 68}{space 3}  .867881
{txt}{space 10}GB  {c |}{col 15}{res}{space 2}        0{col 27}{txt}  (empty)
{space 10}GR  {c |}{col 15}{res}{space 2} -1.41784{col 27}{space 2} .3405988{col 38}{space 1}   -4.16{col 47}{space 3}0.000{col 55}{space 4}-2.085401{col 68}{space 3}-.7502781
{txt}{space 10}IE  {c |}{col 15}{res}{space 2}        0{col 27}{txt}  (empty)
{space 10}IT  {c |}{col 15}{res}{space 2} .2091611{col 27}{space 2} .3846152{col 38}{space 1}    0.54{col 47}{space 3}0.587{col 55}{space 4}-.5446708{col 68}{space 3}  .962993
{txt}{space 10}NL  {c |}{col 15}{res}{space 2} .5985576{col 27}{space 2} .1013599{col 38}{space 1}    5.91{col 47}{space 3}0.000{col 55}{space 4} .3998959{col 68}{space 3} .7972194
{txt}{space 10}NO  {c |}{col 15}{res}{space 2} 1.025705{col 27}{space 2} .2807753{col 38}{space 1}    3.65{col 47}{space 3}0.000{col 55}{space 4} .4753956{col 68}{space 3} 1.576014
{txt}{space 10}PL  {c |}{col 15}{res}{space 2}-3.293417{col 27}{space 2} .6805593{col 38}{space 1}   -4.84{col 47}{space 3}0.000{col 55}{space 4}-4.627289{col 68}{space 3}-1.959546
{txt}{space 10}PT  {c |}{col 15}{res}{space 2}        0{col 27}{txt}  (empty)
{space 10}SE  {c |}{col 15}{res}{space 2}-.6077908{col 27}{space 2} .3372063{col 38}{space 1}   -1.80{col 47}{space 3}0.071{col 55}{space 4}-1.268703{col 68}{space 3} .0531214
{txt}{space 13} {c |}
{space 9}year {c |}
{space 8}2004  {c |}{col 15}{res}{space 2}-.0795035{col 27}{space 2} .4117177{col 38}{space 1}   -0.19{col 47}{space 3}0.847{col 55}{space 4}-.8864554{col 68}{space 3} .7274484
{txt}{space 8}2006  {c |}{col 15}{res}{space 2}-.1054417{col 27}{space 2} .4045118{col 38}{space 1}   -0.26{col 47}{space 3}0.794{col 55}{space 4}-.8982702{col 68}{space 3} .6873869
{txt}{space 8}2008  {c |}{col 15}{res}{space 2}  .312204{col 27}{space 2} .3401124{col 38}{space 1}    0.92{col 47}{space 3}0.359{col 55}{space 4}-.3544042{col 68}{space 3} .9788121
{txt}{space 8}2010  {c |}{col 15}{res}{space 2}  .507225{col 27}{space 2} .3658286{col 38}{space 1}    1.39{col 47}{space 3}0.166{col 55}{space 4} -.209786{col 68}{space 3} 1.224236
{txt}{space 8}2012  {c |}{col 15}{res}{space 2} .3058779{col 27}{space 2}  .310061{col 38}{space 1}    0.99{col 47}{space 3}0.324{col 55}{space 4}-.3018305{col 68}{space 3} .9135862
{txt}{space 8}2014  {c |}{col 15}{res}{space 2} .8473266{col 27}{space 2} .3290845{col 38}{space 1}    2.57{col 47}{space 3}0.010{col 55}{space 4} .2023328{col 68}{space 3}  1.49232
{txt}{space 8}2016  {c |}{col 15}{res}{space 2} .9271746{col 27}{space 2} .3434855{col 38}{space 1}    2.70{col 47}{space 3}0.007{col 55}{space 4} .2539554{col 68}{space 3} 1.600394
{txt}{space 8}2018  {c |}{col 15}{res}{space 2} .6983667{col 27}{space 2} .3323357{col 38}{space 1}    2.10{col 47}{space 3}0.036{col 55}{space 4} .0470008{col 68}{space 3} 1.349733
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2}-4.485684{col 27}{space 2} .3004265{col 38}{space 1}  -14.93{col 47}{space 3}0.000{col 55}{space 4} -5.07451{col 68}{space 3}-3.896859
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)
{com}. 
. // Estimate logistic regression for 'switching2_leftboth' (switching from left parties to nationalist) with various predictors, storing the results
. eststo:  logit switching2_leftboth  meanprobfreyosborne  unemplindiv2 female agea mbtru2 rlgdgr     i.country2 i.year   if west2==1, cluster(countr_year)

{txt}note: {bf:5.country2} != 0 predicts failure perfectly;
      {bf:5.country2} omitted and 10710 obs not used.

note: {bf:11.country2} != 0 predicts failure perfectly;
      {bf:11.country2} omitted and 10003 obs not used.

note: {bf:14.country2} != 0 predicts failure perfectly;
      {bf:14.country2} omitted and 14459 obs not used.

note: {bf:18.country2} != 0 predicts failure perfectly;
      {bf:18.country2} omitted and 14384 obs not used.

note: {bf:28.country2} != 0 predicts failure perfectly;
      {bf:28.country2} omitted and 11095 obs not used.

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1563.1413}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1509.6577}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1472.9223}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1472.4549}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1472.4535}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1472.4535}  
{res}
{txt}Logistic regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:136,250}
{txt}{col 56}{lalign 13:Wald chi2({res:25})}{col 69} = {res}{ralign 7:119.20}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}Log pseudolikelihood = {res:-1472.4535}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.0580}

{txt}{ralign 85:(Std. err. adjusted for {res:93} clusters in countr_year)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}switching2_leftboth{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      z{col 53}   P>|z|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
meanprobfreyosborne {c |}{col 21}{res}{space 2} .8504655{col 33}{space 2} .2264868{col 44}{space 1}    3.76{col 53}{space 3}0.000{col 61}{space 4} .4065595{col 74}{space 3} 1.294371
{txt}{space 7}unemplindiv2 {c |}{col 21}{res}{space 2} -.132904{col 33}{space 2} .3730373{col 44}{space 1}   -0.36{col 53}{space 3}0.722{col 61}{space 4}-.8640436{col 74}{space 3} .5982356
{txt}{space 13}female {c |}{col 21}{res}{space 2}-.1528637{col 33}{space 2} .1364818{col 44}{space 1}   -1.12{col 53}{space 3}0.263{col 61}{space 4}-.4203632{col 74}{space 3} .1146358
{txt}{space 15}agea {c |}{col 21}{res}{space 2}  .005865{col 33}{space 2} .0031748{col 44}{space 1}    1.85{col 53}{space 3}0.065{col 61}{space 4}-.0003575{col 74}{space 3} .0120874
{txt}{space 13}mbtru2 {c |}{col 21}{res}{space 2} .6078688{col 33}{space 2} .1801451{col 44}{space 1}    3.37{col 53}{space 3}0.001{col 61}{space 4} .2547909{col 74}{space 3} .9609467
{txt}{space 13}rlgdgr {c |}{col 21}{res}{space 2} -.019593{col 33}{space 2} .0253582{col 44}{space 1}   -0.77{col 53}{space 3}0.440{col 61}{space 4}-.0692942{col 74}{space 3} .0301081
{txt}{space 19} {c |}
{space 11}country2 {c |}
{space 16}BE  {c |}{col 21}{res}{space 2}-.6184516{col 33}{space 2} .3447621{col 44}{space 1}   -1.79{col 53}{space 3}0.073{col 61}{space 4}-1.294173{col 74}{space 3} .0572697
{txt}{space 16}CH  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}DE  {c |}{col 21}{res}{space 2} -.182404{col 33}{space 2} .7573546{col 44}{space 1}   -0.24{col 53}{space 3}0.810{col 61}{space 4}-1.666792{col 74}{space 3} 1.301984
{txt}{space 16}DK  {c |}{col 21}{res}{space 2} 1.127209{col 33}{space 2} .3486581{col 44}{space 1}    3.23{col 53}{space 3}0.001{col 61}{space 4} .4438522{col 74}{space 3} 1.810567
{txt}{space 16}ES  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}FI  {c |}{col 21}{res}{space 2} .4071342{col 33}{space 2} .3805317{col 44}{space 1}    1.07{col 53}{space 3}0.285{col 61}{space 4}-.3386942{col 74}{space 3} 1.152963
{txt}{space 16}FR  {c |}{col 21}{res}{space 2} .3858092{col 33}{space 2} .3009903{col 44}{space 1}    1.28{col 53}{space 3}0.200{col 61}{space 4}-.2041209{col 74}{space 3} .9757393
{txt}{space 16}GB  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}GR  {c |}{col 21}{res}{space 2}-1.079402{col 33}{space 2} .7205693{col 44}{space 1}   -1.50{col 53}{space 3}0.134{col 61}{space 4}-2.491692{col 74}{space 3}  .332888
{txt}{space 16}IE  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}IT  {c |}{col 21}{res}{space 2}-.4722537{col 33}{space 2} .3335507{col 44}{space 1}   -1.42{col 53}{space 3}0.157{col 61}{space 4}-1.126001{col 74}{space 3} .1814936
{txt}{space 16}NL  {c |}{col 21}{res}{space 2} .3601267{col 33}{space 2} .2997338{col 44}{space 1}    1.20{col 53}{space 3}0.230{col 61}{space 4}-.2273408{col 74}{space 3} .9475942
{txt}{space 16}NO  {c |}{col 21}{res}{space 2} .8920097{col 33}{space 2} .4489083{col 44}{space 1}    1.99{col 53}{space 3}0.047{col 61}{space 4} .0121657{col 74}{space 3} 1.771854
{txt}{space 16}PL  {c |}{col 21}{res}{space 2}-1.437733{col 33}{space 2} .7886622{col 44}{space 1}   -1.82{col 53}{space 3}0.068{col 61}{space 4}-2.983483{col 74}{space 3} .1080163
{txt}{space 16}PT  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}SE  {c |}{col 21}{res}{space 2} .3312037{col 33}{space 2}  .436733{col 44}{space 1}    0.76{col 53}{space 3}0.448{col 61}{space 4}-.5247773{col 74}{space 3} 1.187185
{txt}{space 19} {c |}
{space 15}year {c |}
{space 14}2004  {c |}{col 21}{res}{space 2} .0867694{col 33}{space 2} .5297061{col 44}{space 1}    0.16{col 53}{space 3}0.870{col 61}{space 4}-.9514355{col 74}{space 3} 1.124974
{txt}{space 14}2006  {c |}{col 21}{res}{space 2}-.7511487{col 33}{space 2} .5020525{col 44}{space 1}   -1.50{col 53}{space 3}0.135{col 61}{space 4}-1.735154{col 74}{space 3} .2328561
{txt}{space 14}2008  {c |}{col 21}{res}{space 2} .5282463{col 33}{space 2} .5337547{col 44}{space 1}    0.99{col 53}{space 3}0.322{col 61}{space 4}-.5178936{col 74}{space 3} 1.574386
{txt}{space 14}2010  {c |}{col 21}{res}{space 2} 1.032555{col 33}{space 2} .4291854{col 44}{space 1}    2.41{col 53}{space 3}0.016{col 61}{space 4} .1913673{col 74}{space 3} 1.873743
{txt}{space 14}2012  {c |}{col 21}{res}{space 2} .7513279{col 33}{space 2}  .436241{col 44}{space 1}    1.72{col 53}{space 3}0.085{col 61}{space 4}-.1036888{col 74}{space 3} 1.606345
{txt}{space 14}2014  {c |}{col 21}{res}{space 2} .9898122{col 33}{space 2} .4531707{col 44}{space 1}    2.18{col 53}{space 3}0.029{col 61}{space 4}  .101614{col 74}{space 3}  1.87801
{txt}{space 14}2016  {c |}{col 21}{res}{space 2} 1.283214{col 33}{space 2} .5493012{col 44}{space 1}    2.34{col 53}{space 3}0.019{col 61}{space 4} .2066036{col 74}{space 3} 2.359825
{txt}{space 14}2018  {c |}{col 21}{res}{space 2} .1421439{col 33}{space 2} .5002553{col 44}{space 1}    0.28{col 53}{space 3}0.776{col 61}{space 4}-.8383384{col 74}{space 3} 1.122626
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-8.288748{col 33}{space 2} .4727428{col 44}{space 1}  -17.53{col 53}{space 3}0.000{col 61}{space 4}-9.215307{col 74}{space 3}-7.362189
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)
{com}. eststo:  logit switching2_leftboth  rti  unemplindiv2   female agea  mbtru2 rlgdgr    i.country2 i.year  if west2==1 , cluster(countr_year)

{txt}note: {bf:11.country2} != 0 predicts failure perfectly;
      {bf:11.country2} omitted and 9697 obs not used.

note: {bf:14.country2} != 0 predicts failure perfectly;
      {bf:14.country2} omitted and 16770 obs not used.

note: {bf:18.country2} != 0 predicts failure perfectly;
      {bf:18.country2} omitted and 15405 obs not used.

note: {bf:28.country2} != 0 predicts failure perfectly;
      {bf:28.country2} omitted and 10977 obs not used.

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1611.4134}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1568.2685}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1501.2366}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1498.9337}  
Iteration 4:{space 3}log pseudolikelihood = {res: -1498.879}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1498.8789}  
{res}
{txt}Logistic regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:151,345}
{txt}{col 56}{lalign 13:Wald chi2({res:26})}{col 69} = {res}{ralign 7:154.41}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}Log pseudolikelihood = {res:-1498.8789}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.0698}

{txt}{ralign 85:(Std. err. adjusted for {res:102} clusters in countr_year)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}switching2_leftboth{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      z{col 53}   P>|z|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 16}rti {c |}{col 21}{res}{space 2} .1117399{col 33}{space 2} .0634631{col 44}{space 1}    1.76{col 53}{space 3}0.078{col 61}{space 4}-.0126455{col 74}{space 3} .2361254
{txt}{space 7}unemplindiv2 {c |}{col 21}{res}{space 2}-.0903658{col 33}{space 2} .3310239{col 44}{space 1}   -0.27{col 53}{space 3}0.785{col 61}{space 4}-.7391607{col 74}{space 3} .5584291
{txt}{space 13}female {c |}{col 21}{res}{space 2}-.2224176{col 33}{space 2} .1346172{col 44}{space 1}   -1.65{col 53}{space 3}0.098{col 61}{space 4}-.4862626{col 74}{space 3} .0414273
{txt}{space 15}agea {c |}{col 21}{res}{space 2}  .005813{col 33}{space 2} .0033743{col 44}{space 1}    1.72{col 53}{space 3}0.085{col 61}{space 4}-.0008005{col 74}{space 3} .0124264
{txt}{space 13}mbtru2 {c |}{col 21}{res}{space 2} .6323428{col 33}{space 2} .1818408{col 44}{space 1}    3.48{col 53}{space 3}0.001{col 61}{space 4} .2759415{col 74}{space 3} .9887441
{txt}{space 13}rlgdgr {c |}{col 21}{res}{space 2}-.0078695{col 33}{space 2} .0243422{col 44}{space 1}   -0.32{col 53}{space 3}0.746{col 61}{space 4}-.0555793{col 74}{space 3} .0398403
{txt}{space 19} {c |}
{space 11}country2 {c |}
{space 16}BE  {c |}{col 21}{res}{space 2} -.515207{col 33}{space 2} .4164555{col 44}{space 1}   -1.24{col 53}{space 3}0.216{col 61}{space 4}-1.331445{col 74}{space 3} .3010307
{txt}{space 16}CH  {c |}{col 21}{res}{space 2} -2.43524{col 33}{space 2} 1.062667{col 44}{space 1}   -2.29{col 53}{space 3}0.022{col 61}{space 4} -4.51803{col 74}{space 3}-.3524503
{txt}{space 16}DE  {c |}{col 21}{res}{space 2}-.2898024{col 33}{space 2} .7570804{col 44}{space 1}   -0.38{col 53}{space 3}0.702{col 61}{space 4}-1.773653{col 74}{space 3} 1.194048
{txt}{space 16}DK  {c |}{col 21}{res}{space 2} 1.210466{col 33}{space 2} .3794933{col 44}{space 1}    3.19{col 53}{space 3}0.001{col 61}{space 4} .4666733{col 74}{space 3}  1.95426
{txt}{space 16}ES  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}FI  {c |}{col 21}{res}{space 2} .4548004{col 33}{space 2} .4202377{col 44}{space 1}    1.08{col 53}{space 3}0.279{col 61}{space 4}-.3688504{col 74}{space 3} 1.278451
{txt}{space 16}FR  {c |}{col 21}{res}{space 2} .2791249{col 33}{space 2} .3171439{col 44}{space 1}    0.88{col 53}{space 3}0.379{col 61}{space 4}-.3424657{col 74}{space 3} .9007154
{txt}{space 16}GB  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}GR  {c |}{col 21}{res}{space 2}-1.597696{col 33}{space 2} .7851774{col 44}{space 1}   -2.03{col 53}{space 3}0.042{col 61}{space 4}-3.136616{col 74}{space 3}-.0587768
{txt}{space 16}IE  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}IT  {c |}{col 21}{res}{space 2}-.7346262{col 33}{space 2} .5329947{col 44}{space 1}   -1.38{col 53}{space 3}0.168{col 61}{space 4}-1.779277{col 74}{space 3} .3100243
{txt}{space 16}NL  {c |}{col 21}{res}{space 2} .2289332{col 33}{space 2} .3396042{col 44}{space 1}    0.67{col 53}{space 3}0.500{col 61}{space 4}-.4366789{col 74}{space 3} .8945452
{txt}{space 16}NO  {c |}{col 21}{res}{space 2} .8307377{col 33}{space 2} .4674954{col 44}{space 1}    1.78{col 53}{space 3}0.076{col 61}{space 4}-.0855364{col 74}{space 3} 1.747012
{txt}{space 16}PL  {c |}{col 21}{res}{space 2}-2.480543{col 33}{space 2}  1.05702{col 44}{space 1}   -2.35{col 53}{space 3}0.019{col 61}{space 4}-4.552264{col 74}{space 3} -.408821
{txt}{space 16}PT  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}SE  {c |}{col 21}{res}{space 2} .2942847{col 33}{space 2} .4756272{col 44}{space 1}    0.62{col 53}{space 3}0.536{col 61}{space 4}-.6379275{col 74}{space 3} 1.226497
{txt}{space 19} {c |}
{space 15}year {c |}
{space 14}2004  {c |}{col 21}{res}{space 2} .0237251{col 33}{space 2} .5759515{col 44}{space 1}    0.04{col 53}{space 3}0.967{col 61}{space 4}-1.105119{col 74}{space 3} 1.152569
{txt}{space 14}2006  {c |}{col 21}{res}{space 2}-.2739874{col 33}{space 2} .5203556{col 44}{space 1}   -0.53{col 53}{space 3}0.599{col 61}{space 4}-1.293866{col 74}{space 3} .7458908
{txt}{space 14}2008  {c |}{col 21}{res}{space 2} .7078191{col 33}{space 2} .4974549{col 44}{space 1}    1.42{col 53}{space 3}0.155{col 61}{space 4}-.2671745{col 74}{space 3} 1.682813
{txt}{space 14}2010  {c |}{col 21}{res}{space 2} .9765229{col 33}{space 2} .4584223{col 44}{space 1}    2.13{col 53}{space 3}0.033{col 61}{space 4} .0780316{col 74}{space 3} 1.875014
{txt}{space 14}2012  {c |}{col 21}{res}{space 2} .7789925{col 33}{space 2} .4535215{col 44}{space 1}    1.72{col 53}{space 3}0.086{col 61}{space 4}-.1098934{col 74}{space 3} 1.667878
{txt}{space 14}2014  {c |}{col 21}{res}{space 2} 1.094758{col 33}{space 2} .4696161{col 44}{space 1}    2.33{col 53}{space 3}0.020{col 61}{space 4} .1743272{col 74}{space 3} 2.015189
{txt}{space 14}2016  {c |}{col 21}{res}{space 2} 1.490887{col 33}{space 2} .5311382{col 44}{space 1}    2.81{col 53}{space 3}0.005{col 61}{space 4} .4498757{col 74}{space 3} 2.531899
{txt}{space 14}2018  {c |}{col 21}{res}{space 2} .0583439{col 33}{space 2} .5996905{col 44}{space 1}    0.10{col 53}{space 3}0.922{col 61}{space 4}-1.117028{col 74}{space 3} 1.233716
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-7.904056{col 33}{space 2} .5161489{col 44}{space 1}  -15.31{col 53}{space 3}0.000{col 61}{space 4} -8.91569{col 74}{space 3}-6.892423
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)
{com}. eststo:  logit switching2_leftboth  task3cog2and3  unemplindiv2    female agea mbtru2 rlgdgr     i.country2 i.year  if west2==1 , cluster(countr_year)

{txt}note: {bf:11.country2} != 0 predicts failure perfectly;
      {bf:11.country2} omitted and 11028 obs not used.

note: {bf:14.country2} != 0 predicts failure perfectly;
      {bf:14.country2} omitted and 18354 obs not used.

note: {bf:18.country2} != 0 predicts failure perfectly;
      {bf:18.country2} omitted and 17616 obs not used.

note: {bf:28.country2} != 0 predicts failure perfectly;
      {bf:28.country2} omitted and 12485 obs not used.

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -1758.891}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1706.2746}  
Iteration 2:{space 3}log pseudolikelihood = {res: -1634.432}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1632.3871}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1632.3358}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1632.3357}  
{res}
{txt}Logistic regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:172,359}
{txt}{col 56}{lalign 13:Wald chi2({res:26})}{col 69} = {res}{ralign 7:149.52}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}Log pseudolikelihood = {res:-1632.3357}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.0720}

{txt}{ralign 85:(Std. err. adjusted for {res:102} clusters in countr_year)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}switching2_leftboth{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      z{col 53}   P>|z|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}task3cog2and3 {c |}{col 21}{res}{space 2} .8331219{col 33}{space 2} .1572794{col 44}{space 1}    5.30{col 53}{space 3}0.000{col 61}{space 4} .5248599{col 74}{space 3} 1.141384
{txt}{space 7}unemplindiv2 {c |}{col 21}{res}{space 2}-.1288449{col 33}{space 2} .3511857{col 44}{space 1}   -0.37{col 53}{space 3}0.714{col 61}{space 4}-.8171563{col 74}{space 3} .5594664
{txt}{space 13}female {c |}{col 21}{res}{space 2}-.2127116{col 33}{space 2} .1311879{col 44}{space 1}   -1.62{col 53}{space 3}0.105{col 61}{space 4}-.4698351{col 74}{space 3}  .044412
{txt}{space 15}agea {c |}{col 21}{res}{space 2} .0054528{col 33}{space 2} .0029745{col 44}{space 1}    1.83{col 53}{space 3}0.067{col 61}{space 4}-.0003771{col 74}{space 3} .0112826
{txt}{space 13}mbtru2 {c |}{col 21}{res}{space 2} .6445192{col 33}{space 2}  .175813{col 44}{space 1}    3.67{col 53}{space 3}0.000{col 61}{space 4} .2999321{col 74}{space 3} .9891062
{txt}{space 13}rlgdgr {c |}{col 21}{res}{space 2}-.0194295{col 33}{space 2} .0225376{col 44}{space 1}   -0.86{col 53}{space 3}0.389{col 61}{space 4}-.0636025{col 74}{space 3} .0247434
{txt}{space 19} {c |}
{space 11}country2 {c |}
{space 16}BE  {c |}{col 21}{res}{space 2}-.6209569{col 33}{space 2} .3829359{col 44}{space 1}   -1.62{col 53}{space 3}0.105{col 61}{space 4}-1.371498{col 74}{space 3} .1295837
{txt}{space 16}CH  {c |}{col 21}{res}{space 2}-2.529982{col 33}{space 2} 1.055154{col 44}{space 1}   -2.40{col 53}{space 3}0.016{col 61}{space 4}-4.598046{col 74}{space 3}-.4619178
{txt}{space 16}DE  {c |}{col 21}{res}{space 2}-.3800831{col 33}{space 2} .7600067{col 44}{space 1}   -0.50{col 53}{space 3}0.617{col 61}{space 4}-1.869669{col 74}{space 3} 1.109503
{txt}{space 16}DK  {c |}{col 21}{res}{space 2}  1.12229{col 33}{space 2}  .349186{col 44}{space 1}    3.21{col 53}{space 3}0.001{col 61}{space 4} .4378983{col 74}{space 3} 1.806682
{txt}{space 16}ES  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}FI  {c |}{col 21}{res}{space 2} .3624334{col 33}{space 2} .3821182{col 44}{space 1}    0.95{col 53}{space 3}0.343{col 61}{space 4}-.3865044{col 74}{space 3} 1.111371
{txt}{space 16}FR  {c |}{col 21}{res}{space 2} .3271742{col 33}{space 2} .2972455{col 44}{space 1}    1.10{col 53}{space 3}0.271{col 61}{space 4}-.2554162{col 74}{space 3} .9097647
{txt}{space 16}GB  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}GR  {c |}{col 21}{res}{space 2}-1.215997{col 33}{space 2} .7723634{col 44}{space 1}   -1.57{col 53}{space 3}0.115{col 61}{space 4}-2.729801{col 74}{space 3} .2978079
{txt}{space 16}IE  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}IT  {c |}{col 21}{res}{space 2}-.6371253{col 33}{space 2} .3570413{col 44}{space 1}   -1.78{col 53}{space 3}0.074{col 61}{space 4}-1.336913{col 74}{space 3} .0626629
{txt}{space 16}NL  {c |}{col 21}{res}{space 2}  .206385{col 33}{space 2} .2952586{col 44}{space 1}    0.70{col 53}{space 3}0.485{col 61}{space 4}-.3723112{col 74}{space 3} .7850812
{txt}{space 16}NO  {c |}{col 21}{res}{space 2}  .779211{col 33}{space 2}  .439666{col 44}{space 1}    1.77{col 53}{space 3}0.076{col 61}{space 4}-.0825184{col 74}{space 3}  1.64094
{txt}{space 16}PL  {c |}{col 21}{res}{space 2}-1.560229{col 33}{space 2} .7859211{col 44}{space 1}   -1.99{col 53}{space 3}0.047{col 61}{space 4}-3.100606{col 74}{space 3}-.0198523
{txt}{space 16}PT  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}SE  {c |}{col 21}{res}{space 2} .1619102{col 33}{space 2} .4509397{col 44}{space 1}    0.36{col 53}{space 3}0.720{col 61}{space 4}-.7219154{col 74}{space 3} 1.045736
{txt}{space 19} {c |}
{space 15}year {c |}
{space 14}2004  {c |}{col 21}{res}{space 2} .0881891{col 33}{space 2} .5533066{col 44}{space 1}    0.16{col 53}{space 3}0.873{col 61}{space 4}-.9962719{col 74}{space 3}  1.17265
{txt}{space 14}2006  {c |}{col 21}{res}{space 2} -.392354{col 33}{space 2} .5178222{col 44}{space 1}   -0.76{col 53}{space 3}0.449{col 61}{space 4}-1.407267{col 74}{space 3} .6225589
{txt}{space 14}2008  {c |}{col 21}{res}{space 2} .6392859{col 33}{space 2} .4994687{col 44}{space 1}    1.28{col 53}{space 3}0.201{col 61}{space 4}-.3396547{col 74}{space 3} 1.618226
{txt}{space 14}2010  {c |}{col 21}{res}{space 2}  .926229{col 33}{space 2} .4507911{col 44}{space 1}    2.05{col 53}{space 3}0.040{col 61}{space 4} .0426946{col 74}{space 3} 1.809763
{txt}{space 14}2012  {c |}{col 21}{res}{space 2} .7732465{col 33}{space 2} .4414194{col 44}{space 1}    1.75{col 53}{space 3}0.080{col 61}{space 4}-.0919196{col 74}{space 3} 1.638413
{txt}{space 14}2014  {c |}{col 21}{res}{space 2} 1.075287{col 33}{space 2} .4723706{col 44}{space 1}    2.28{col 53}{space 3}0.023{col 61}{space 4} .1494574{col 74}{space 3} 2.001116
{txt}{space 14}2016  {c |}{col 21}{res}{space 2} 1.428088{col 33}{space 2} .5337892{col 44}{space 1}    2.68{col 53}{space 3}0.007{col 61}{space 4} .3818804{col 74}{space 3} 2.474296
{txt}{space 14}2018  {c |}{col 21}{res}{space 2} .1555754{col 33}{space 2}  .535933{col 44}{space 1}    0.29{col 53}{space 3}0.772{col 61}{space 4}-.8948339{col 74}{space 3} 1.205985
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-8.367574{col 33}{space 2} .5287025{col 44}{space 1}  -15.83{col 53}{space 3}0.000{col 61}{space 4}-9.403812{col 74}{space 3}-7.331336
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)
{com}. 
. // Generate an output table with specific variables, saving it in the current results
.    esttab       ///
>                 , keep(meanprobfreyosborne task3cog2and3 rti) replace label se compress nogap star(* 0.1 ** 0.05 *** 0.01) ///
>                 b(%6.3f) scalars( "N Observations"  )   ///
>                 indicate("Demographics = *male*" "Socio-econ = *mbtru2*"   "Country FE = *country*" "Year FE = *year", label($\checkmark$ ))    ///
>                 noconstant nonotes nomtitles nodepvars
{res}
{txt}{hline 94}
{txt}                       (1)          (2)          (3)          (4)          (5)          (6)   
{txt}{hline 94}
{res}main                                                                                          {txt}
{txt}Computerizatio~){res}     0.626***                               0.850***                          {txt}
                {res} {ralign 9:{txt:(}0.077{txt:)}}                              {ralign 9:{txt:(}0.226{txt:)}}                             {txt}
{txt}RTI             {res}                  0.044*                                 0.112*               {txt}
                {res}              {ralign 9:{txt:(}0.024{txt:)}}                              {ralign 9:{txt:(}0.063{txt:)}}                {txt}
{txt}Routine         {res}                               0.698***                               0.833***{txt}
                {res}                           {ralign 9:{txt:(}0.056{txt:)}}                              {ralign 9:{txt:(}0.157{txt:)}}   {txt}
{txt}Demographics    {res} $\check~$    $\check~$    $\check~$    $\check~$    $\check~$    $\check~$   {txt}
{txt}Socio-econ      {res} $\check~$    $\check~$    $\check~$    $\check~$    $\check~$    $\check~$   {txt}
{txt}Country FE      {res} $\check~$    $\check~$    $\check~$    $\check~$    $\check~$    $\check~$   {txt}
{txt}Year FE         {res} $\check~$    $\check~$    $\check~$    $\check~$    $\check~$    $\check~$   {txt}
{txt}{hline 94}
{txt}Observations    {res}    148688       153158       174298       136250       151345       172359   {txt}
{txt}{hline 94}
{com}.                 
. 
.    esttab using "Table\ESS_switching.tex", ///
>                  keep(meanprobfreyosborne task3cog2and3 rti) replace label se compress nogap star(* 0.1 ** 0.05 *** 0.01) ///
>                 b(%6.3f) scalars( "N Observations"  )   ///
>                 indicate("Demographics = *male*" "Socio-econ = *mbtru2*"   "Country FE = *country*" "Year FE = *year", label($\checkmark$ ))    ///
>                 noconstant nonotes nomtitles nodepvars
{res}{txt}(output written to {browse  `"Table\ESS_switching.tex"'})
{com}. 
. {c )-}
{txt}
{com}. 
{txt}end of do-file

{com}. do "./Do/2_1_Rally_US.do"
{txt}
{com}. *****************************************************************************
. *                                 Analysis Rallies by MSA in the US                                     *
. *                                                                                                                                                       *                       
. * Author:                       Valentina Gonzalez Rostani                                                      *
. * Contact:                      mag384@pitt.edu                                                                 *
. * Date:                         August 19 2024                                                                                  *
. * Version:                      Stata 17                                                                                                *                                                                          
. *                                                                                                                                                       *
. *****************************************************************************
. /*
> This do-file:
> - Creates table A13 using data collected for rallies, visits, and exposure to automation. 
> 
> Input:
> - Data\Rally_Visits_MSA.dta
> 
> Output:
> - Table 1: Trump's Campaign Strategy (Close election 5) [Table\Trump_high_close5.tex]
> - Table A13: Trump's Campaing Strategy (Close election 10) [Table\Trump_high_close10.tex]
> - Table A14: Trump's Campaing Strategy (Forecasting 2016) [Table\Trump_high_forec.tex]
> - Table A12: Summary statistics of variables used in this study about Trump's campaign strategies: rallies [Table\US_rallies_descriptive.tex]
> */
. 
. *Defining Directory
. cd "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication"
{res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication
{txt}
{com}. 
. *Calling the data
. use "Data\Rally_Visits_MSA.dta", clear 
{txt}
{com}. 
. 
. //////////////////////////////////////
> * Preparing Variables 
. //////////////////////////////////////
> {c -(}
. 
. global statesID AK AL AR AZ CA CO CT DC DE FL GA HI IA ID IL IN KS KY LA MA MD ME MI MN MO MS MT NC ND NE NH NJ NM NV NY OH OK OR PA RI SC SD TN TX UT VA VT WA WI WV WY
. 
. 
. 
. 
. gen rallies_pop=(rallies/Population)*100000 // Relative to the population and by 100K individuals for easier interpretation. This allows for comparisons between areas or groups with different population sizes by standardizing the number of visits according to a common population size. 
. gen visits_pop=(visited/Population)*100000
. gen anti_pop=(anti/Population)*100000
. 
. gen high_pop_pop=(high_pop/Population) // Share of exposed workers 
. 
. lab var high_pop "Workers Exposed to Automation"
. lab var Pop "Population"
. lab var anti_pop "Hate Incidents Per 100K Pop"
. lab var high_pop_pop "Workers Exposed to Automation"
. lab var close_election "Close Elections"
. {c )-}
{txt}
{com}. 
. //////////////////////////////////////
> * Regression Analysis 
. //////////////////////////////////////
> {c -(}
. 
. // table 1: Trump's Campaign Strategy
. {c -(}
. gen interaction_pop5=high_pop_pop*close_election5
. gen interaction2_pop5=high_pop_pop*close_election5
. gen interaction3_pop5=high_pop_pop*anti_pop
. gen interaction4_pop5=close_election5*anti_pop
. 
. lab var interaction_pop5 "Exposed x Close Elections"
. lab var interaction2_pop5 "Exposed x Close Elections"
. lab var interaction3_pop5 "Exposed x Hate Incidents"
. lab var interaction4_pop5 "Hate Incidents x Close"
. lab var close_election5 "Close Elections"
. 
. 
. eststo clear
. 
. eststo: qui reg rallies_pop high_pop_pop close_election5  anti_pop $statesID , cluster(state_num)
{txt}({res}est1{txt} stored)
{com}. eststo: qui reg rallies_pop high_pop_pop close_election5   interaction2_pop5  anti_pop $statesID ,cluster(state_num)
{txt}({res}est2{txt} stored)
{com}. eststo: qui reg rallies_pop high_pop_pop close_election5   interaction3_pop5  anti_pop $statesID ,cluster(state_num)
{txt}({res}est3{txt} stored)
{com}. eststo: qui reg rallies_pop high_pop_pop close_election5  interaction2_pop5 interaction3_pop5 interaction4_pop5  anti_pop $statesID   ,cluster(state_num)
{txt}({res}est4{txt} stored)
{com}. 
. esttab , replace label se title(Trump's Campaing Strategy \label {c -(}TableRallies{c )-})  mti("Simple" "Close" "Hate" "All")  compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(high* anti_pop close_election* interaction*) scalars( "N Observations" "r2 R$^2$" "aic AIC" )  indicate("FE State = *AK") 
{res}
{txt}Trump's Campaing Strategy \label {TableRallies}
{txt}{hline 68}
{txt}                       (1)          (2)          (3)          (4)   
{txt}                    Simple        Close         Hate          All   
{txt}{hline 68}
{txt}Workers Expose~n{res}     0.194***     0.177**      0.169**      0.155** {txt}
                {res} {ralign 9:{txt:(}0.071{txt:)}}    {ralign 9:{txt:(}0.070{txt:)}}    {ralign 9:{txt:(}0.065{txt:)}}    {ralign 9:{txt:(}0.066{txt:)}}   {txt}
{txt}Close Elections {res}     0.005*       0.001        0.007**      0.002   {txt}
                {res} {ralign 9:{txt:(}0.003{txt:)}}    {ralign 9:{txt:(}0.005{txt:)}}    {ralign 9:{txt:(}0.003{txt:)}}    {ralign 9:{txt:(}0.005{txt:)}}   {txt}
{txt}Hate Incidents~p{res}    -0.052*      -0.051*       0.015        0.015   {txt}
                {res} {ralign 9:{txt:(}0.029{txt:)}}    {ralign 9:{txt:(}0.029{txt:)}}    {ralign 9:{txt:(}0.031{txt:)}}    {ralign 9:{txt:(}0.035{txt:)}}   {txt}
{txt}Exposed x Clos~s{res}                  0.344***                  0.331***{txt}
                {res}              {ralign 9:{txt:(}0.071{txt:)}}                 {ralign 9:{txt:(}0.091{txt:)}}   {txt}
{txt}Exposed x Hate~s{res}                              -0.259       -0.249   {txt}
                {res}                           {ralign 9:{txt:(}0.156{txt:)}}    {ralign 9:{txt:(}0.164{txt:)}}   {txt}
{txt}Hate Incidents~e{res}                                           -0.033   {txt}
                {res}                                        {ralign 9:{txt:(}0.031{txt:)}}   {txt}
{txt}FE State        {res}       Yes          Yes          Yes          Yes   {txt}
{txt}{hline 68}
{txt}Observations    {res}       381          381          381          381   {txt}
{txt}R$^2$           {res}     0.661        0.674        0.681        0.689   {txt}
{txt}AIC             {res}  -2.2e+03     -2.2e+03     -2.2e+03     -2.3e+03   {txt}
{txt}{hline 68}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}. 
. esttab using "Table\Trump_high_close5.tex", replace label se title(Trump's Campaing Strategy \label {c -(}TableRallies{c )-})  mti("Simple" "Close" "Hate" "All")   compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(high* anti_pop close_election* interaction*)  indicate("FE State = *AK" ) scalars( "N Observations" "r2 R$^2$" "aic AIC" )
{res}{txt}(output written to {browse  `"Table\Trump_high_close5.tex"'})
{com}. 
. 
. {c )-}
. 
. // table A13: Trump's Campaing Strategy (Close election 10)
. {c -(}
. gen interaction_pop=high_pop_pop*close_election
. gen interaction2_pop=high_pop_pop*close_election
. gen interaction3_pop=high_pop_pop*anti_pop
. gen interaction4_pop=close_election*anti_pop
. 
. 
. lab var interaction_pop "Exposed x Close Elections"
. lab var interaction2_pop "Exposed x Close Elections"
. lab var interaction3_pop "Exposed x Hate Incidents"
. lab var interaction4_pop "Hate Incidents x Close"
. 
. 
. eststo clear
. 
. eststo: qui reg rallies_pop high_pop_pop close_election  anti_pop $statesID , cluster(state_num)
{txt}({res}est1{txt} stored)
{com}. eststo: qui reg rallies_pop high_pop_pop close_election interaction2_pop  anti_pop $statesID ,cluster(state_num)
{txt}({res}est2{txt} stored)
{com}. eststo: qui reg rallies_pop high_pop_pop close_election   interaction3_pop  anti_pop $statesID ,cluster(state_num)
{txt}({res}est3{txt} stored)
{com}. eststo: qui reg rallies_pop high_pop_pop close_election  interaction2_pop interaction3_pop interaction4_pop  anti_pop $statesID ,cluster(state_num)
{txt}({res}est4{txt} stored)
{com}. 
. esttab , replace label se title(Trump's Campaing Strategy \label {c -(}TableRallies{c )-})  mti("Simple" "Close" "Hate" "All") compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(high* anti_pop close_election interaction*) scalars( "N Observations" "r2 R$^2$" "aic AIC" )  indicate("FE State = *AK") 
{res}
{txt}Trump's Campaing Strategy \label {TableRallies}
{txt}{hline 68}
{txt}                       (1)          (2)          (3)          (4)   
{txt}                    Simple        Close         Hate          All   
{txt}{hline 68}
{txt}Workers Expose~n{res}     0.191***     0.052*       0.167***     0.062   {txt}
                {res} {ralign 9:{txt:(}0.067{txt:)}}    {ralign 9:{txt:(}0.029{txt:)}}    {ralign 9:{txt:(}0.062{txt:)}}    {ralign 9:{txt:(}0.039{txt:)}}   {txt}
{txt}Close Elections {res}     0.016**      0.006        0.015**      0.006   {txt}
                {res} {ralign 9:{txt:(}0.006{txt:)}}    {ralign 9:{txt:(}0.004{txt:)}}    {ralign 9:{txt:(}0.006{txt:)}}    {ralign 9:{txt:(}0.004{txt:)}}   {txt}
{txt}Hate Incidents~p{res}    -0.048*      -0.040        0.016       -0.023   {txt}
                {res} {ralign 9:{txt:(}0.028{txt:)}}    {ralign 9:{txt:(}0.026{txt:)}}    {ralign 9:{txt:(}0.030{txt:)}}    {ralign 9:{txt:(}0.050{txt:)}}   {txt}
{txt}Exposed x Clos~s{res}                  0.301***                  0.238***{txt}
                {res}              {ralign 9:{txt:(}0.077{txt:)}}                 {ralign 9:{txt:(}0.088{txt:)}}   {txt}
{txt}Exposed x Hate~s{res}                              -0.249       -0.075   {txt}
                {res}                           {ralign 9:{txt:(}0.156{txt:)}}    {ralign 9:{txt:(}0.185{txt:)}}   {txt}
{txt}Hate Incidents~e{res}                                            0.016   {txt}
                {res}                                        {ralign 9:{txt:(}0.038{txt:)}}   {txt}
{txt}FE State        {res}       Yes          Yes          Yes          Yes   {txt}
{txt}{hline 68}
{txt}Observations    {res}       381          381          381          381   {txt}
{txt}R$^2$           {res}     0.671        0.727        0.689        0.731   {txt}
{txt}AIC             {res}  -2.2e+03     -2.3e+03     -2.3e+03     -2.3e+03   {txt}
{txt}{hline 68}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}. 
. esttab using "Table\Trump_high_close10.tex", replace label se title(Trump's Campaing Strategy (Close election 10) \label {c -(}TableRallies10{c )-})  mti("Simple" "Close" "Hate" "All")  compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(high* anti_pop close_election* interaction*)  indicate("FE State = *AK" ) scalars( "N Observations" "r2 R$^2$" "aic AIC" )
{res}{txt}(output written to {browse  `"Table\Trump_high_close10.tex"'})
{com}. 
. {c )-}
. 
. 
. //table A14: Trump's Campaing Strategy (Forecasting 2016)
. {c -(}
. gen interaction_pop_f=high_pop_pop*forescasting2
. gen interaction2_pop_f=high_pop_pop*forescasting2
. gen interaction3_pop_f=high_pop_pop*anti_pop
. gen interaction4_pop_f=forescasting2*anti_pop
. 
. lab var interaction_pop_f "Exposed x Close Elections"
. lab var interaction2_pop_f "Exposed x Close Elections"
. lab var interaction3_pop_f "Exposed x Hate Incidents"
. lab var interaction4_pop_f "Hate Incidents x Close"
. lab var forescasting2 "Close Elections"
. 
. eststo clear
. 
. eststo: qui reg rallies_pop high_pop_pop forescasting2  anti_pop $statesID , cluster(state_num)
{txt}({res}est1{txt} stored)
{com}. eststo: qui reg rallies_pop high_pop_pop forescasting2 interaction2_pop_f  anti_pop $statesID ,cluster(state_num)
{txt}({res}est2{txt} stored)
{com}. eststo: qui reg rallies_pop high_pop_pop forescasting2   interaction3_pop_f  anti_pop $statesID ,cluster(state_num)
{txt}({res}est3{txt} stored)
{com}. eststo: qui reg rallies_pop high_pop_pop forescasting2  interaction2_pop_f interaction3_pop_f interaction4_pop_f  anti_pop $statesID ,cluster(state_num)
{txt}({res}est4{txt} stored)
{com}. 
. esttab , replace label se title(Trump's Campaing Strategy \label {c -(}TableRallies{c )-})  mti("Simple" "Close" "Hate" "All")  compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(high* anti_pop forescasting2 interaction*) scalars( "N Observations" "r2 R$^2$" "aic AIC" )   indicate("FE State = *AK") 
{res}
{txt}Trump's Campaing Strategy \label {TableRallies}
{txt}{hline 68}
{txt}                       (1)          (2)          (3)          (4)   
{txt}                    Simple        Close         Hate          All   
{txt}{hline 68}
{txt}Workers Expose~n{res}     0.192***     0.162**      0.168**      0.140** {txt}
                {res} {ralign 9:{txt:(}0.071{txt:)}}    {ralign 9:{txt:(}0.065{txt:)}}    {ralign 9:{txt:(}0.065{txt:)}}    {ralign 9:{txt:(}0.061{txt:)}}   {txt}
{txt}Close Elections {res}    -0.019***    -0.025***    -0.019***    -0.025***{txt}
                {res} {ralign 9:{txt:(}0.006{txt:)}}    {ralign 9:{txt:(}0.006{txt:)}}    {ralign 9:{txt:(}0.005{txt:)}}    {ralign 9:{txt:(}0.006{txt:)}}   {txt}
{txt}Hate Incidents~p{res}    -0.051*      -0.049*       0.015        0.017   {txt}
                {res} {ralign 9:{txt:(}0.030{txt:)}}    {ralign 9:{txt:(}0.029{txt:)}}    {ralign 9:{txt:(}0.031{txt:)}}    {ralign 9:{txt:(}0.035{txt:)}}   {txt}
{txt}Exposed x Clos~s{res}                  0.315***                  0.353***{txt}
                {res}              {ralign 9:{txt:(}0.055{txt:)}}                 {ralign 9:{txt:(}0.080{txt:)}}   {txt}
{txt}Exposed x Hate~s{res}                              -0.256       -0.252   {txt}
                {res}                           {ralign 9:{txt:(}0.156{txt:)}}    {ralign 9:{txt:(}0.165{txt:)}}   {txt}
{txt}Hate Incidents~e{res}                                           -0.043   {txt}
                {res}                                        {ralign 9:{txt:(}0.034{txt:)}}   {txt}
{txt}FE State        {res}       Yes          Yes          Yes          Yes   {txt}
{txt}{hline 68}
{txt}Observations    {res}       381          381          381          381   {txt}
{txt}R$^2$           {res}     0.662        0.678        0.681        0.694   {txt}
{txt}AIC             {res}  -2.2e+03     -2.2e+03     -2.2e+03     -2.3e+03   {txt}
{txt}{hline 68}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}. 
. esttab using "Table\Trump_high_forec.tex", replace label se title(Trump's Campaing Strategy (Forecasting 2016) \label {c -(}TableRallies{c )-})  mti("Simple" "Close" "Hate" "All")  compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(high* anti_pop forescasting2 interaction*)  indicate("FE State = *AK" ) scalars( "N Observations" "r2 R$^2$" "aic AIC" )
{res}{txt}(output written to {browse  `"Table\Trump_high_forec.tex"'})
{com}. 
. {c )-}
. {c )-}
{txt}
{com}. 
. ////////////////////////////////////
> * Descriptives
. ///////////////////////////////////
> {c -(}
. lab var rallies "\# Rallies per MSA"
. lab var visited "Visit MSA (dummy)"
. 
. lab var rallies_pop "\# Rallies relative to population"
. lab var visits_pop "Visit (dummy) relative to population"
. 
. lab var close_election5 "Close election 2012 (5\%)"
. lab var forescasting2 "Close election - Forecasting 2016"
. 
. lab var close_election "Close election 2012 (10\%)"
. lab var anti "\# Hate incident per MSA"
. lab var high_pop_pop "Workers Exposed to Automation (relative to pop.)"
. lab var high "Workers Exposed to Automation (relative to MSA)"
. lab var high_pop "\# Workers Exposed to Automation per MSA"
. 
. // table A12: Summary statistics of variables used in this study about Trump's campaign strategies: rallies
. {c -(}
. eststo clear
. 
. estpost sum rallies rallies_pop visited visits_pop high_pop high high_pop_pop  anti anti_pop close_election5 forescasting2 close_election, d

{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(count)}{space 1}{space 1}{ralign 9:e(sum_w)}{space 1}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(Var)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(skewn~)}{space 1}{space 1}{ralign 9:e(kurto~)}{space 1}{space 1}{ralign 9:e(sum)}{space 1}{space 1}{ralign 9:e(min)}{space 1}{space 1}{ralign 9:e(max)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:rallies}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf:  .351706}}}{space 1}{space 1}{ralign 9:{res:{sf:  .665451}}}{space 1}{space 1}{ralign 9:{res:{sf: .8157518}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.595102}}}{space 1}{space 1}{ralign 9:{res:{sf:  9.30922}}}{space 1}{space 1}{ralign 9:{res:{sf:      134}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        4}}}{space 1}
{space 0}{space 0}{ralign 12:rallies_pop}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf: .0057695}}}{space 1}{space 1}{ralign 9:{res:{sf: .0004297}}}{space 1}{space 1}{ralign 9:{res:{sf:  .020728}}}{space 1}{space 1}{ralign 9:{res:{sf: 8.221068}}}{space 1}{space 1}{ralign 9:{res:{sf: 97.00657}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.198184}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .2866909}}}{space 1}
{space 0}{space 0}{ralign 12:visited}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf: .2047244}}}{space 1}{space 1}{ralign 9:{res:{sf: .1632408}}}{space 1}{space 1}{ralign 9:{res:{sf: .4040307}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.463571}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.142041}}}{space 1}{space 1}{ralign 9:{res:{sf:       78}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:visits_pop}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf: .0032997}}}{space 1}{space 1}{ralign 9:{res:{sf: .0000963}}}{space 1}{space 1}{ralign 9:{res:{sf: .0098134}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.748024}}}{space 1}{space 1}{ralign 9:{res:{sf: 29.51865}}}{space 1}{space 1}{ralign 9:{res:{sf:  1.25719}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .0721844}}}{space 1}
{space 0}{space 0}{ralign 12:high_pop}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf: 182502.4}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.49e+11}}}{space 1}{space 1}{ralign 9:{res:{sf: 386542.9}}}{space 1}{space 1}{ralign 9:{res:{sf: 5.873373}}}{space 1}{space 1}{ralign 9:{res:{sf: 47.62439}}}{space 1}{space 1}{ralign 9:{res:{sf: 6.95e+07}}}{space 1}{space 1}{ralign 9:{res:{sf: 14190.64}}}{space 1}{space 1}{ralign 9:{res:{sf:  4128796}}}{space 1}
{space 0}{space 0}{ralign 12:high}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf: .2586352}}}{space 1}{space 1}{ralign 9:{res:{sf: .0010531}}}{space 1}{space 1}{ralign 9:{res:{sf: .0324518}}}{space 1}{space 1}{ralign 9:{res:{sf: .6938782}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.791499}}}{space 1}{space 1}{ralign 9:{res:{sf:    98.54}}}{space 1}{space 1}{ralign 9:{res:{sf:     .177}}}{space 1}{space 1}{ralign 9:{res:{sf:     .421}}}{space 1}
{space 0}{space 0}{ralign 12:high_pop_pop}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf: .0241831}}}{space 1}{space 1}{ralign 9:{res:{sf: .0016938}}}{space 1}{space 1}{ralign 9:{res:{sf: .0411556}}}{space 1}{space 1}{ralign 9:{res:{sf:  3.84817}}}{space 1}{space 1}{ralign 9:{res:{sf: 21.28739}}}{space 1}{space 1}{ralign 9:{res:{sf: 9.213745}}}{space 1}{space 1}{ralign 9:{res:{sf: .0008939}}}{space 1}{space 1}{ralign 9:{res:{sf: .2955413}}}{space 1}
{space 0}{space 0}{ralign 12:anti}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.745407}}}{space 1}{space 1}{ralign 9:{res:{sf: 396.9534}}}{space 1}{space 1}{ralign 9:{res:{sf: 19.92369}}}{space 1}{space 1}{ralign 9:{res:{sf: 12.69748}}}{space 1}{space 1}{ralign 9:{res:{sf: 193.6343}}}{space 1}{space 1}{ralign 9:{res:{sf:     1427}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:      329}}}{space 1}
{space 0}{space 0}{ralign 12:anti_pop}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf: .0449558}}}{space 1}{space 1}{ralign 9:{res:{sf: .0397521}}}{space 1}{space 1}{ralign 9:{res:{sf: .1993793}}}{space 1}{space 1}{ralign 9:{res:{sf: 9.505856}}}{space 1}{space 1}{ralign 9:{res:{sf: 107.3085}}}{space 1}{space 1}{ralign 9:{res:{sf: 17.12815}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.459054}}}{space 1}
{space 0}{space 0}{ralign 12:close_elec~5}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf: .1496063}}}{space 1}{space 1}{ralign 9:{res:{sf: .1275591}}}{space 1}{space 1}{ralign 9:{res:{sf: .3571541}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.964723}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.860136}}}{space 1}{space 1}{ralign 9:{res:{sf:       57}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:forescasti~2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf: .1076115}}}{space 1}{space 1}{ralign 9:{res:{sf:  .096284}}}{space 1}{space 1}{ralign 9:{res:{sf: .3102967}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.532444}}}{space 1}{space 1}{ralign 9:{res:{sf: 7.413271}}}{space 1}{space 1}{ralign 9:{res:{sf:       41}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:close_elec~n}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf:      381}}}{space 1}{space 1}{ralign 9:{res:{sf: .4199475}}}{space 1}{space 1}{ralign 9:{res:{sf: .2442326}}}{space 1}{space 1}{ralign 9:{res:{sf:  .494199}}}{space 1}{space 1}{ralign 9:{res:{sf: .3243947}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.105232}}}{space 1}{space 1}{ralign 9:{res:{sf:      160}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(p1)}{space 1}{space 1}{ralign 9:e(p5)}{space 1}{space 1}{ralign 9:e(p10)}{space 1}{space 1}{ralign 9:e(p25)}{space 1}{space 1}{ralign 9:e(p50)}{space 1}{space 1}{ralign 9:e(p75)}{space 1}{space 1}{ralign 9:e(p90)}{space 1}{space 1}{ralign 9:e(p95)}{space 1}{space 1}{ralign 9:e(p99)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:rallies}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        2}}}{space 1}{space 1}{ralign 9:{res:{sf:        4}}}{space 1}
{space 0}{space 0}{ralign 12:rallies_pop}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .0171235}}}{space 1}{space 1}{ralign 9:{res:{sf: .0339412}}}{space 1}{space 1}{ralign 9:{res:{sf: .0773192}}}{space 1}
{space 0}{space 0}{ralign 12:visited}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:visits_pop}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:  .009966}}}{space 1}{space 1}{ralign 9:{res:{sf: .0171235}}}{space 1}{space 1}{ralign 9:{res:{sf: .0716727}}}{space 1}
{space 0}{space 0}{ralign 12:high_pop}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 19120.13}}}{space 1}{space 1}{ralign 9:{res:{sf: 23385.29}}}{space 1}{space 1}{ralign 9:{res:{sf: 29371.43}}}{space 1}{space 1}{ralign 9:{res:{sf: 38412.68}}}{space 1}{space 1}{ralign 9:{res:{sf: 63114.92}}}{space 1}{space 1}{ralign 9:{res:{sf: 151136.7}}}{space 1}{space 1}{ralign 9:{res:{sf: 387383.8}}}{space 1}{space 1}{ralign 9:{res:{sf: 683704.6}}}{space 1}{space 1}{ralign 9:{res:{sf:  1993358}}}{space 1}
{space 0}{space 0}{ralign 12:high}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     .186}}}{space 1}{space 1}{ralign 9:{res:{sf:     .209}}}{space 1}{space 1}{ralign 9:{res:{sf:     .222}}}{space 1}{space 1}{ralign 9:{res:{sf:     .238}}}{space 1}{space 1}{ralign 9:{res:{sf:     .255}}}{space 1}{space 1}{ralign 9:{res:{sf:     .277}}}{space 1}{space 1}{ralign 9:{res:{sf:     .299}}}{space 1}{space 1}{ralign 9:{res:{sf:     .313}}}{space 1}{space 1}{ralign 9:{res:{sf:      .35}}}{space 1}
{space 0}{space 0}{ralign 12:high_pop_pop}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0009439}}}{space 1}{space 1}{ralign 9:{res:{sf: .0016598}}}{space 1}{space 1}{ralign 9:{res:{sf: .0024265}}}{space 1}{space 1}{ralign 9:{res:{sf: .0043918}}}{space 1}{space 1}{ralign 9:{res:{sf: .0092813}}}{space 1}{space 1}{ralign 9:{res:{sf: .0222307}}}{space 1}{space 1}{ralign 9:{res:{sf: .0663798}}}{space 1}{space 1}{ralign 9:{res:{sf:  .093607}}}{space 1}{space 1}{ralign 9:{res:{sf: .2465741}}}{space 1}
{space 0}{space 0}{ralign 12:anti}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        2}}}{space 1}{space 1}{ralign 9:{res:{sf:        7}}}{space 1}{space 1}{ralign 9:{res:{sf:       11}}}{space 1}{space 1}{ralign 9:{res:{sf:       97}}}{space 1}
{space 0}{space 0}{ralign 12:anti_pop}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .0170125}}}{space 1}{space 1}{ralign 9:{res:{sf: .0871139}}}{space 1}{space 1}{ralign 9:{res:{sf: .1835516}}}{space 1}{space 1}{ralign 9:{res:{sf: .8247379}}}{space 1}
{space 0}{space 0}{ralign 12:close_elec~5}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:forescasti~2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:close_elec~n}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{com}. 
. esttab , ///
>         cells("mean(label(Mean) fmt(2)) p50(label(Median) fmt(2)) sd(label(S.D.) fmt(2)) min(label(Min.) fmt(2)) max(label(Max) fmt(2)) count(label(Obs.) fmt(0))") ///
>         nonumber label replace noobs
{res}
{txt}{hline 98}
{txt}                                                                                                  
{txt}                             Mean       Median         S.D.         Min.          Max         Obs.
{txt}{hline 98}
{txt}\# Rallies per MSA  {res}         0.35         0.00         0.82         0.00         4.00          381{txt}
{txt}\# Rallies relativ~o{res}         0.01         0.00         0.02         0.00         0.29          381{txt}
{txt}Visit MSA (dummy)   {res}         0.20         0.00         0.40         0.00         1.00          381{txt}
{txt}Visit (dummy) rela~a{res}         0.00         0.00         0.01         0.00         0.07          381{txt}
{txt}\# Workers Exposed~n{res}    182502.35     63114.92    386542.94     14190.64   4128796.25          381{txt}
{txt}Workers Exposed to~r{res}         0.26         0.26         0.03         0.18         0.42          381{txt}
{txt}Workers Expos..)    {res}         0.02         0.01         0.04         0.00         0.30          381{txt}
{txt}\# Hate incident p~A{res}         3.75         0.00        19.92         0.00       329.00          381{txt}
{txt}Hate Incidents Per~p{res}         0.04         0.00         0.20         0.00         2.46          381{txt}
{txt}Close election 201~){res}         0.15         0.00         0.36         0.00         1.00          381{txt}
{txt}Close election -~201{res}         0.11         0.00         0.31         0.00         1.00          381{txt}
{txt}Close election 201~){res}         0.42         0.00         0.49         0.00         1.00          381{txt}
{txt}{hline 98}
{com}. 
. 
. esttab using "Table\US_rallies_descriptive.tex", ///
>         cells("mean(label(Mean) fmt(2)) p50(label(Median) fmt(2)) sd(label(S.D.) fmt(2)) min(label(Min.) fmt(2)) max(label(Max) fmt(2)) count(label(Obs.) fmt(0))") ///
>         nonumber label replace noobs
{res}{txt}(output written to {browse  `"Table\US_rallies_descriptive.tex"'})
{com}. {c )-}
. {c )-}
{txt}
{com}. 
{txt}end of do-file

{com}. do "./Do/2_2_Speech_US.do"
{txt}
{com}. *****************************************************************************
. *                                 Analysis Speech by MSA in the US                                      *
. *                                                                                                                                                       *                       
. * Author:                       Valentina Gonzalez Rostani                                                      *
. * Contact:                      mag384@pitt.edu                                                                 *
. * Date:                         August 9 2024                                                                                   *
. * Version:                      Stata 17                                                                                                *                                                                          
. *                                                                                                                                                       *
. *****************************************************************************
. /*
> This do-file:
> - Creates Table 2 and A15 using data from Trump Speeches. 
> 
> Input:
> - Data\Text\combined_df.csv // This file contains the data from speeches
> - Data\Rally_Visits_MSA.dta // This file contains information about the MSA (e.g, number of exposed workers)
> - Alternatively you can go to line 45 and use prepared data: 
>         - Data\Speech_MSA.dta
> 
> Output:
> - Table 2: Trump's Campaign Strategy: Speeches [Table\Trump_text_IVchanged.tex]
> - Table A15: Trump's Campaing Strategy: Speeches (Total count) [Table\Trump_text_IVchanged_count.tex]
> 
> */
. 
. *Defining Directory
. cd "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication"
{res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication
{txt}
{com}. 
. * Merging MSA data with speeches - alternatively go to line 45
. {c -(}
. import delimited "Data\Text\combined_df.csv", clear
{res}{txt}(encoding automatically selected: UTF-8)
{text}(28 vars, 98 obs)
{com}. merge m:m msa_state using "Data\Rally_Visits_MSA.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             321
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}             321{txt}  (_merge==2)

{col 5}Matched{col 30}{res}              98{txt}  (_merge==3)
{col 5}{hline 41}
{com}. keep if _merge==3
{txt}(321 observations deleted)
{com}. drop _merge 
. 
. // Generating the variables of interest regarding exposure of workers and hate crimes
.  
. gen high_pop_pop=(high_pop/Population) // Share of exposed workers 
. gen anti_pop=(anti/Population)*100000 
. 
. keep state  word_count  msa_state  pro_worker_count  culture_count  veryclose10 foreign  month  high_pop_pop   anti_pop 
. save "Data\Speech_MSA.dta", replace
{txt}{p 0 4 2}
file {bf}
Data\Speech_MSA.dta{rm}
saved
{p_end}
{com}. 
. {c )-}
{txt}
{com}. 
. * Alternatively you can call directly the data
. use "Data\Speech_MSA.dta", clear 
{txt}
{com}. 
. *******************************************************************************
. * Preparing variables
. *******************************************************************************
. {c -(}
. encode state, generate(state_num2)  // Encode 'state' as numeric
. encode msa_state, generate(msa_num)  // Encode 'msa_state' as numeric
. 
. // Calculate word shares
. gen pro_w = pro_worker_count / word_count  // Pro-worker word share
. gen pro_c = culture_count / word_count  // Pro-culture word share
. 
. // Create interaction terms
. gen int_exp_close = high_pop_pop * veryclose10  // Interaction: exposure x closeness
. gen int_exp_anti = high_pop_pop * anti_pop  // Interaction: exposure x hate incidents
. 
. // Label variables
. lab var veryclose10 "Close"  
. lab var int_exp_close "Exposed x Close"
. lab var int_exp_anti "Exposed x Hate"
. lab var high_pop_pop "Workers Exp. to Auto."
. lab var anti_pop "Hate Inc.x 100K Pop"
. 
. {c )-}
{txt}
{com}. *****************************************
. * Regression
. *****************************************
. {c -(}
. 
. //table 2: Trump's Campaign Strategy: Speeches
. {c -(}
. preserve  // Preserve the current dataset
. 
. keep if msa_num ~= .  // Keep observations with non-missing 'msa_num' (0 observations deleted)
{txt}(0 observations deleted)
{com}. 
. eststo clear  // Clear any previously stored estimates
. 
. // DV: Share of pro-worker rhetoric
. eststo: qui reg pro_w high_pop_pop veryclose10 i.month foreign anti_pop  i.state_num2, cluster(state_num2)
{txt}({res}est1{txt} stored)
{com}. eststo: qui reg pro_w high_pop_pop veryclose10 int_exp_close  i.month foreign anti_pop  i.state_num2, cluster(state_num2)
{txt}({res}est2{txt} stored)
{com}. eststo: qui reg pro_w high_pop_pop veryclose10 int_exp_close int_exp_anti i.month foreign anti_pop  i.state_num2,cluster(state_num2)
{txt}({res}est3{txt} stored)
{com}. 
. // DV: Share of pro-culture rhetoric
. eststo: qui reg pro_c high_pop_pop veryclose10 i.month foreign anti_pop i.state_num2 ,cluster(state_num2)
{txt}({res}est4{txt} stored)
{com}. eststo: qui reg pro_c high_pop_pop veryclose10 int_exp_close i.month foreign anti_pop  i.state_num2, cluster(state_num2)
{txt}({res}est5{txt} stored)
{com}. eststo: qui reg pro_c high_pop_pop veryclose10 int_exp_close int_exp_anti i.month foreign anti_pop  i.state_num2,cluster(state_num2)
{txt}({res}est6{txt} stored)
{com}. 
. // Create regression table
. esttab , replace label se ///
>     title("Trump's Campaign Strategy \label {c -(}TableSpeech2{c )-}") ///
>     compress nogap ///
>     star(* 0.1 ** 0.05 *** 0.01) ///
>     b(%6.3f) ///
>     keep(high_pop* anti* *close* int*) ///
>     scalars("N Observations" "r2 R$^2$" "aic AIC") ///
>     indicate("FE State = *state*" "Foreign = foreign*" "FE Month = *month") ///
>         nomtitle collabels(none) mgroups("Pro-worker Rhetoric (1-3)" "Cultural Rhetoric (4-6)", pattern(1 0 0 1 0 0)  ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span)
{res}
{txt}Trump's Campaign Strategy \label {TableSpeech2}
{txt}{hline 94}
{txt}                 \multicolumn{3}{c}{Pro-worker Rhetoric (1-3)} \multicolumn{3}{c}{Cultural Rhetoric (4-6)}
{txt}                       (1)          (2)          (3)          (4)          (5)          (6)   
{txt}{hline 94}
{txt}Workers E.. to~.{res}     0.444***    -2.874***    -2.693***     0.024**      0.043        0.044   {txt}
                {res} {ralign 9:{txt:(}0.150{txt:)}}    {ralign 9:{txt:(}0.557{txt:)}}    {ralign 9:{txt:(}0.512{txt:)}}    {ralign 9:{txt:(}0.011{txt:)}}    {ralign 9:{txt:(}0.080{txt:)}}    {ralign 9:{txt:(}0.082{txt:)}}   {txt}
{txt}Close           {res}    -0.041***    -0.447***    -0.410***     0.007***     0.009        0.009   {txt}
                {res} {ralign 9:{txt:(}0.012{txt:)}}    {ralign 9:{txt:(}0.069{txt:)}}    {ralign 9:{txt:(}0.059{txt:)}}    {ralign 9:{txt:(}0.001{txt:)}}    {ralign 9:{txt:(}0.010{txt:)}}    {ralign 9:{txt:(}0.010{txt:)}}   {txt}
{txt}Hate Inc.x 100~p{res}    -0.047**     -0.051**      0.157**     -0.004**     -0.004**     -0.002   {txt}
                {res} {ralign 9:{txt:(}0.021{txt:)}}    {ralign 9:{txt:(}0.019{txt:)}}    {ralign 9:{txt:(}0.057{txt:)}}    {ralign 9:{txt:(}0.001{txt:)}}    {ralign 9:{txt:(}0.001{txt:)}}    {ralign 9:{txt:(}0.004{txt:)}}   {txt}
{txt}Exposed x Close {res}                  3.361***     3.060***                 -0.019       -0.021   {txt}
                {res}              {ralign 9:{txt:(}0.515{txt:)}}    {ralign 9:{txt:(}0.446{txt:)}}                 {ralign 9:{txt:(}0.081{txt:)}}    {ralign 9:{txt:(}0.084{txt:)}}   {txt}
{txt}Exposed x Hate  {res}                              -0.778***                              -0.007   {txt}
                {res}                           {ralign 9:{txt:(}0.210{txt:)}}                              {ralign 9:{txt:(}0.012{txt:)}}   {txt}
{txt}FE State        {res}       Yes          Yes          Yes          Yes          Yes          Yes   {txt}
{txt}Foreign         {res}       Yes          Yes          Yes          Yes          Yes          Yes   {txt}
{txt}FE Month        {res}       Yes          Yes          Yes          Yes          Yes          Yes   {txt}
{txt}{hline 94}
{txt}Observations    {res}        98           98           98           98           98           98   {txt}
{txt}R$^2$           {res}     0.503        0.537        0.571        0.336        0.336        0.337   {txt}
{txt}AIC             {res}  -320.397     -327.377     -332.854     -828.802     -828.840     -826.928   {txt}
{txt}{hline 94}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}.         
. // Create regression table
. esttab using "Table\Trump_text_IVchanged.tex", replace label se ///
>     title("Trump's Campaign Strategy \label {c -(}TableSpeech2{c )-}") ///
>     compress nogap ///
>     star(* 0.1 ** 0.05 *** 0.01) ///
>     b(%6.3f) ///
>     keep(high_pop* anti* *close* int*) ///
>     scalars("N Observations" "r2 R$^2$" "aic AIC") ///
>     indicate("FE State = *state*" "Foreign = foreign*" "FE Month = *month") ///
>         nomtitle collabels(none) mgroups("Pro-worker Rhetoric (1-3)" "Cultural Rhetoric (4-6)", pattern(1 0 0 1 0 0)  ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span)
{res}{txt}(output written to {browse  `"Table\Trump_text_IVchanged.tex"'})
{com}. 
. 
. // Save regression table to a .tex file
. 
. 
. restore  // Restore the original dataset
. 
. /////
> {c )-}
. 
. // table A15: Trump's Campaing Strategy: Speeches (Total count)
. {c -(}
. preserve  // Preserve the current dataset
. 
. keep if msa_num ~= .  // Keep observations with non-missing 'msa_num' (0 observations deleted)
{txt}(0 observations deleted)
{com}. 
. eststo clear  // Clear any previously stored estimates
. 
. ********* Now number of words counts instead of share of words **************
. eststo clear
. eststo: qui reg pro_worker_count high_pop_pop  i.month foreign anti_pop i.state_num2 ,cluster(state_num2)
{txt}({res}est1{txt} stored)
{com}. eststo: qui reg pro_worker_count high_pop_pop veryclose10 foreign i.month  anti_pop  i.state_num2, cluster(state_num2)
{txt}({res}est2{txt} stored)
{com}. eststo: qui reg pro_worker_count high_pop_pop veryclose10 foreign int_exp_close  i.month  anti_pop  i.state_num2, cluster(state_num2)
{txt}({res}est3{txt} stored)
{com}. eststo: qui reg pro_worker_count high_pop_pop veryclose10 foreign int_exp_close int_exp_anti i.month  anti_pop  i.state_num2,cluster(state_num2)
{txt}({res}est4{txt} stored)
{com}. 
. eststo: qui reg culture_count high_pop_pop  i.month foreign anti_pop  i.state_num2 ,cluster(state_num2)
{txt}({res}est5{txt} stored)
{com}. eststo: qui reg culture_count high_pop_pop veryclose10 foreign i.month  anti_pop i.state_num2 ,cluster(state_num2)
{txt}({res}est6{txt} stored)
{com}. eststo: qui reg culture_count high_pop_pop veryclose10 int_exp_close foreign i.month  anti_pop  i.state_num2, cluster(state_num2)
{txt}({res}est7{txt} stored)
{com}. eststo: qui reg culture_count high_pop_pop veryclose10 int_exp_close foreign int_exp_anti i.month  anti_pop  i.state_num2,cluster(state_num2)
{txt}({res}est8{txt} stored)
{com}. 
. 
. esttab , replace label se title(Trump's Campaing Strategy: Speeches (Total count) \label {c -(}TableTotal{c )-})  nomtitle collabels(none) mgroups("Pro-worker Rhetoric (1-4)" "Cultural Rhetoric (5-8)", pattern(1 0 0 0 1 0 0 0)  ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span) compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.2f) keep(high_pop* anti* *close* int*)  scalars( "N Observations" "r2 R$^2$" "aic AIC" ) indicate("FE State = *state*" "Foreign = foreign*" "Fe Month =*month") 
{res}
{txt}Trump's Campaing Strategy: Speeches (Total count) \label {TableTotal}
{txt}{hline 120}
{txt}                 \multicolumn{4}{c}{Pro-worker Rhetoric (1-4)}       \multicolumn{4}{c}{Cultural Rhetoric (5-8)}        
{txt}                       (1)          (2)          (3)          (4)          (5)          (6)          (7)          (8)   
{txt}{hline 120}
{txt}Workers E.. to~.{res}    636.18       636.18     -4017.86***  -3658.56***    -12.97       -12.97       399.21       377.56   {txt}
                {res} {ralign 9:{txt:(}450.61{txt:)}}    {ralign 9:{txt:(}450.61{txt:)}}    {ralign 9:{txt:(}1375.41{txt:)}}    {ralign 9:{txt:(}1246.38{txt:)}}    {ralign 9:{txt:(}40.57{txt:)}}    {ralign 9:{txt:(}40.57{txt:)}}    {ralign 9:{txt:(}234.13{txt:)}}    {ralign 9:{txt:(}231.52{txt:)}}   {txt}
{txt}Hate Inc.x 100~p{res}   -119.44*     -119.44*     -124.44*      287.08        -3.23        -3.23        -2.79       -27.59   {txt}
                {res} {ralign 9:{txt:(}67.36{txt:)}}    {ralign 9:{txt:(}67.36{txt:)}}    {ralign 9:{txt:(}62.78{txt:)}}    {ralign 9:{txt:(}277.98{txt:)}}    {ralign 9:{txt:(}5.17{txt:)}}    {ralign 9:{txt:(}5.17{txt:)}}    {ralign 9:{txt:(}4.61{txt:)}}    {ralign 9:{txt:(}16.76{txt:)}}   {txt}
{txt}Close           {res}                 198.14***   -371.74**    -297.68*                    63.79***    114.26***    109.80***{txt}
                {res}              {ralign 9:{txt:(}28.05{txt:)}}    {ralign 9:{txt:(}168.17{txt:)}}    {ralign 9:{txt:(}144.97{txt:)}}                 {ralign 9:{txt:(}4.69{txt:)}}    {ralign 9:{txt:(}29.17{txt:)}}    {ralign 9:{txt:(}29.43{txt:)}}   {txt}
{txt}Exposed x Close {res}                             4714.86***   4116.91***                             -417.56*     -381.54*  {txt}
                {res}                           {ralign 9:{txt:(}1243.99{txt:)}}    {ralign 9:{txt:(}1063.07{txt:)}}                              {ralign 9:{txt:(}217.72{txt:)}}    {ralign 9:{txt:(}220.61{txt:)}}   {txt}
{txt}Exposed x Hate  {res}                                         -1542.64                                               92.95*  {txt}
                {res}                                        {ralign 9:{txt:(}985.93{txt:)}}                                           {ralign 9:{txt:(}51.69{txt:)}}   {txt}
{txt}FE State        {res}       Yes          Yes          Yes          Yes          Yes          Yes          Yes          Yes   {txt}
{txt}Foreign         {res}       Yes          Yes          Yes          Yes          Yes          Yes          Yes          Yes   {txt}
{txt}Fe Month        {res}       Yes          Yes          Yes          Yes          Yes          Yes          Yes          Yes   {txt}
{txt}{hline 120}
{txt}Observations    {res}        98           98           98           98           98           98           98           98   {txt}
{txt}R$^2$           {res}      0.38         0.38         0.40         0.42         0.50         0.50         0.51         0.51   {txt}
{txt}AIC             {res}   1222.34      1222.34      1220.39      1218.40       785.24       785.24       783.93       784.70   {txt}
{txt}{hline 120}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}. 
. esttab using "Table\Trump_text_IVchanged_count.tex", replace label se title(Trump's Campaing Strategy: Speeches (Total count) \label {c -(}TableTotal{c )-})  nomtitle collabels(none) mgroups("Pro-worker Rhetoric (1-4)" "Cultural Rhetoric (5-8)", pattern(1 0 0 0 1 0 0 0)  ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span)  compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.2f) keep(high_pop* anti* *close* int*)  scalars( "N Observations" "r2 R$^2$" "aic AIC" ) indicate("FE State = *state*" "Foreign = foreign*" "FE Month =*month") 
{res}{txt}(output written to {browse  `"Table\Trump_text_IVchanged_count.tex"'})
{com}. 
. 
. restore
. 
. /////
> {c )-}
. {c )-}
{txt}
{com}. 
{txt}end of do-file

{com}. do "./Do/3_1_Regional_Germany.do"
{txt}
{com}. *****************************************************************************
. *         Analysis Regional Exposure to Automation and Hate incidents Germany   *
. *                                                                                                                                                       *                       
. * Author:                       Valentina Gonzalez Rostani                                                      *
. * Contact:                      mag384@pitt.edu                                                                 *
. * Date:                         August 21 2024                                                                                  *
. * Version:                      Stata 17                                                                                                       
. *                                                                                                                                                       *
. *****************************************************************************
. /*
> This do-file:
> - Creates Table 4 and A16 using data from electoral perfomance of the AfD, hate incidents, and exposure to automation. 
> 
> Input:
> - Data\Region_Germany\btw2017kreis (3).csv // This file contains electoral results. 
> - Data\Region_Germany\ RegionEntries14.dta // This file contains replication data from  "Trade and Manufacturing Jobs in Germany" By Wolfgang Dauth, Sebastian Findeisen, and Jens Suedekum
> - Data\Region_Germany\final_aggregated_data.dta// This file contains hate incidents in Germany, subset prepared from ARVIG data. The rmd which creates this file is named as  3_0_Regional_Germany_HateIncidents.rmd
> 
> - Alternatively you can go to line 79 and use prepared data: 
>         - Data\Regional_Germany.dta
> 
> Output:
> - Table 4: AfD Performance [Table\AfD_high_pop_r2.tex]
> - Table A16: Summary statistics of variables used in this study about AfD regional performance [Table\AfD_descriptive.tex]
> 
> */
. 
. *Defining Directory
. cd "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication"
{res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication
{txt}
{com}. 
. * Merging and preparing data - alternatively go to line 79
. {c -(}
. // Electoral data
. {c -(}
. import delimited "Data\Region_Germany\btw2017kreis (3).csv", varnames(5) clear 
{res}{txt}(encoding automatically selected: ISO-8859-1)
{text}(81 vars, 404 obs)
{com}. drop if _n == 1
{txt}(1 observation deleted)
{com}. destring wahlberechtigte wähler ungültige gültige cdu spd dielinke grüne csu fdp afd npd freiewähler bp volksabstimmung pdv mlpd büso sgp dierechte tierschutzallianz b dkp diegrauen du mg menschlichewelt gesundheitsforschung vpartei dievioletten familie diefrauen mieterpartei neueliberale unabhängige übrige, replace
{txt}wahlberechtigte already numeric; no {res}replace
{txt}wähler already numeric; no {res}replace
{txt}ungültige: all characters numeric; {res}replaced {txt}as {res}long
{txt}gültige: all characters numeric; {res}replaced {txt}as {res}long
{txt}cdu: all characters numeric; {res}replaced {txt}as {res}long
{txt}spd: all characters numeric; {res}replaced {txt}as {res}long
{txt}dielinke: all characters numeric; {res}replaced {txt}as {res}long
{txt}grüne: all characters numeric; {res}replaced {txt}as {res}long
{txt}csu: all characters numeric; {res}replaced {txt}as {res}long
{txt}fdp: all characters numeric; {res}replaced {txt}as {res}long
{txt}afd: all characters numeric; {res}replaced {txt}as {res}long
{txt}npd: all characters numeric; {res}replaced {txt}as {res}long
{txt}freiewähler: all characters numeric; {res}replaced {txt}as {res}long
{txt}bp: all characters numeric; {res}replaced {txt}as {res}long
{txt}volksabstimmung: all characters numeric; {res}replaced {txt}as {res}int
{txt}pdv: all characters numeric; {res}replaced {txt}as {res}int
{txt}mlpd: all characters numeric; {res}replaced {txt}as {res}long
{txt}büso: all characters numeric; {res}replaced {txt}as {res}int
{txt}sgp: all characters numeric; {res}replaced {txt}as {res}int
{txt}dierechte: all characters numeric; {res}replaced {txt}as {res}int
{txt}tierschutzallianz: all characters numeric; {res}replaced {txt}as {res}int
{txt}b: all characters numeric; {res}replaced {txt}as {res}int
{txt}dkp: all characters numeric; {res}replaced {txt}as {res}int
{txt}diegrauen: all characters numeric; {res}replaced {txt}as {res}int
{txt}du: all characters numeric; {res}replaced {txt}as {res}int
{txt}mg: all characters numeric; {res}replaced {txt}as {res}int
{txt}menschlichewelt: all characters numeric; {res}replaced {txt}as {res}int
{txt}gesundheitsforschung: all characters numeric; {res}replaced {txt}as {res}int
{txt}vpartei: all characters numeric; {res}replaced {txt}as {res}int
{txt}dievioletten: all characters numeric; {res}replaced {txt}as {res}int
{txt}familie: all characters numeric; {res}replaced {txt}as {res}int
{txt}diefrauen: all characters numeric; {res}replaced {txt}as {res}int
{txt}mieterpartei: all characters numeric; {res}replaced {txt}as {res}int
{txt}neueliberale: all characters numeric; {res}replaced {txt}as {res}int
{txt}unabhängige: all characters numeric; {res}replaced {txt}as {res}int
{txt}übrige: all characters numeric; {res}replaced {txt}as {res}long
{com}. 
. gen afp_prop = afd /gültige // valid vote
. 
. rename statistischekennziffer kreis
{res}{com}. {c )-}
. // Regional exposure of workers - Dauth et al 
. {c -(} 
. merge 1:1 kreis using "Data\Region_Germany\RegionEntries14.dta"
{res}{txt}{p 0 7 2}
(variable
{bf:kreis} was {bf:int}, now {bf:float} to accommodate using data's values)
{p_end}

{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}               5
{txt}{col 9}from master{col 30}{res}               3{txt}  (_merge==1)
{col 9}from using{col 30}{res}               2{txt}  (_merge==2)

{col 5}Matched{col 30}{res}             400{txt}  (_merge==3)
{col 5}{hline 41}
{com}. 
. drop if _merge<3 
{txt}(5 observations deleted)
{com}. drop _merge
. {c )-}
. // Hate incidents Arvig
. {c -(}
. merge 1:1 kreis using "Data\Region_Germany\final_aggregated_data.dta", force
{res}{txt}{p 0 7 2}
(variable
{bf:kreis} was {bf:float}, now {bf:double} to accommodate using data's values)
{p_end}

{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}              84
{txt}{col 9}from master{col 30}{res}              84{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}             316{txt}  (_merge==3)
{col 5}{hline 41}
{com}.  
. replace anti=0 if anti==.
{txt}(84 real changes made)
{com}. {c )-}
. // Preparing clean file 
. {c -(}
. keep afd gültige afp_prop kreis anti routine  pop  perc_hq perc_foreign perc_female  perc_manuf_trad_nocars perc_manuf_auto  reg_south reg_east reg_north   state_n 
. 
. lab var perc_hq "Employment share of workers with University degree (\%)"
. lab var perc_foreign "Employment share of Foreign Born (\%)"
. lab var perc_female  "Employment share of Female (\%)"
. lab var perc_manuf_trad_nocars "Employment share of other manuf. (\%)"
. lab var  perc_manuf_auto "Employment share of manuf. of cars (\%)"
. lab var afp_prop "AfD Share of votes"
. lab var reg_south "South Region"
. lab var reg_east "East Region"
. lab var reg_north "North Region"
. lab var anti "\# Hate incidents per district"
. lab var routine "\# Routine workers"
. lab var pop "Population"
. lab var state_n "State number"
. 
. save "Data\Regional_Germany.dta", replace
{txt}{p 0 4 2}
file {bf}
Data\Regional_Germany.dta{rm}
saved
{p_end}
{com}. {c )-}
. {c )-}
{txt}
{com}. 
. * Alternatively you can call directly the data
. use "Data\Regional_Germany.dta", clear
{txt}
{com}. 
. *******************************************************************************
. * Preparing variables
. *******************************************************************************
. {c -(}
. gen rou_pop=(routine/pop)
. gen anti_pop=(anti/pop)*1000
. gen interaction_pop=anti_pop*rou_pop
. 
. lab var rou_pop "Share of  exposed workers"
. lab var anti_pop "Hate Incidents Per 1K Pop"
. lab var interaction_pop "Exposed x Hate"
. 
. {c )-}
{txt}
{com}. 
. *******************************************************************************
. * Analysis
. *******************************************************************************
. // Table 4: AfD Performance
. {c -(}
. global controls perc_hq perc_foreign perc_female  perc_manuf_trad_nocars perc_manuf_auto 
. 
. eststo clear
. 
. eststo: qui reg afp_prop rou_pop anti_pop , cluster(state_n) 
{txt}({res}est1{txt} stored)
{com}. 
. eststo: qui reg afp_prop rou_pop anti_pop perc_foreign $controls reg_south reg_east reg_north i.state_n, cluster(state_n) 
{txt}({res}est2{txt} stored)
{com}. 
. eststo: qui reg afp_prop rou_pop anti_pop interaction_pop     reg_south reg_east reg_north perc_foreign  $controls, cluster(state_n)
{txt}({res}est3{txt} stored)
{com}. eststo: qui reg afp_prop rou_pop anti_pop interaction_pop     reg_south reg_east reg_north  i.state_n perc_foreign $controls, cluster(state_n)
{txt}({res}est4{txt} stored)
{com}. 
. 
. esttab , replace label se title(AfD Performance \label {c -(}TableAfd{c )-})   compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(rou_pop anti_pop interaction* )     indicate( "Other controls = perc_foreign" "FE State = *state_n" ) scalars( "N Observations" "r2 R$^2$" "aic AIC" )
{res}
{txt}AfD Performance \label {TableAfd}
{txt}{hline 68}
{txt}                       (1)          (2)          (3)          (4)   
{txt}                 AfD Sha~s    AfD Sha~s    AfD Sha~s    AfD Sha~s   
{txt}{hline 68}
{txt}Share of  expo~s{res}    -0.220        0.294*      -0.035        0.004   {txt}
                {res} {ralign 9:{txt:(}0.219{txt:)}}    {ralign 9:{txt:(}0.142{txt:)}}    {ralign 9:{txt:(}0.182{txt:)}}    {ralign 9:{txt:(}0.186{txt:)}}   {txt}
{txt}Hate Incidents~p{res}     0.654***    -0.042       -1.618***    -1.226*  {txt}
                {res} {ralign 9:{txt:(}0.115{txt:)}}    {ralign 9:{txt:(}0.090{txt:)}}    {ralign 9:{txt:(}0.527{txt:)}}    {ralign 9:{txt:(}0.655{txt:)}}   {txt}
{txt}Exposed x Hate  {res}                              18.772***    14.033*  {txt}
                {res}                           {ralign 9:{txt:(}5.879{txt:)}}    {ralign 9:{txt:(}7.039{txt:)}}   {txt}
{txt}Other controls  {res}        No          Yes          Yes          Yes   {txt}
{txt}FE State        {res}        No          Yes           No          Yes   {txt}
{txt}{hline 68}
{txt}Observations    {res}       400          400          400          400   {txt}
{txt}R$^2$           {res}     0.144        0.699        0.603        0.704   {txt}
{txt}AIC             {res}  -1.2e+03     -1.6e+03     -1.5e+03     -1.6e+03   {txt}
{txt}{hline 68}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}. 
. esttab using "Table\AfD_high_pop_r2.tex", replace label se title(AfD Performance \label {c -(}TableAfd{c )-})   compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(rou_pop anti_pop interaction* )     indicate( "Other controls = perc_foreign" "FE State = *state_n" ) scalars( "N Observations" "r2 R$^2$" "aic AIC" ) nomtitle collabels(none) 
{res}{txt}(output written to {browse  `"Table\AfD_high_pop_r2.tex"'})
{com}. 
.         
. {c )-}
{txt}
{com}. *******************************************************************************
. * Descriptives
. *******************************************************************************
. {c -(}
. 
. eststo clear
. 
. // table A16: Summary statistics of variables used in this study about AfD regional performance
. {c -(}
. estpost sum afp_prop rou_pop anti_pop  anti    reg_south reg_east reg_north  $controls, d

{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(count)}{space 1}{space 1}{ralign 9:e(sum_w)}{space 1}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(Var)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(skewn~)}{space 1}{space 1}{ralign 9:e(kurto~)}{space 1}{space 1}{ralign 9:e(sum)}{space 1}{space 1}{ralign 9:e(min)}{space 1}{space 1}{ralign 9:e(max)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:afp_prop}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf: .1211117}}}{space 1}{space 1}{ralign 9:{res:{sf: .0034863}}}{space 1}{space 1}{ralign 9:{res:{sf:  .059045}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.125577}}}{space 1}{space 1}{ralign 9:{res:{sf: 5.004269}}}{space 1}{space 1}{ralign 9:{res:{sf: 48.44469}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .3739725}}}{space 1}
{space 0}{space 0}{ralign 12:rou_pop}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf: .0828056}}}{space 1}{space 1}{ralign 9:{res:{sf: .0001862}}}{space 1}{space 1}{ralign 9:{res:{sf: .0136461}}}{space 1}{space 1}{ralign 9:{res:{sf: .8652342}}}{space 1}{space 1}{ralign 9:{res:{sf: 5.779402}}}{space 1}{space 1}{ralign 9:{res:{sf: 33.12225}}}{space 1}{space 1}{ralign 9:{res:{sf: .0404605}}}{space 1}{space 1}{ralign 9:{res:{sf: .1502379}}}{space 1}
{space 0}{space 0}{ralign 12:anti_pop}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf:  .023344}}}{space 1}{space 1}{ralign 9:{res:{sf: .0011464}}}{space 1}{space 1}{ralign 9:{res:{sf: .0338583}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.482263}}}{space 1}{space 1}{ralign 9:{res:{sf: 34.58685}}}{space 1}{space 1}{ralign 9:{res:{sf: 9.337588}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .3618418}}}{space 1}
{space 0}{space 0}{ralign 12:anti}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf:   4.3325}}}{space 1}{space 1}{ralign 9:{res:{sf: 88.73879}}}{space 1}{space 1}{ralign 9:{res:{sf: 9.420127}}}{space 1}{space 1}{ralign 9:{res:{sf: 11.17956}}}{space 1}{space 1}{ralign 9:{res:{sf: 171.4371}}}{space 1}{space 1}{ralign 9:{res:{sf:     1733}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:      156}}}{space 1}
{space 0}{space 0}{ralign 12:reg_south}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf:      .35}}}{space 1}{space 1}{ralign 9:{res:{sf: .2280702}}}{space 1}{space 1}{ralign 9:{res:{sf: .4775669}}}{space 1}{space 1}{ralign 9:{res:{sf: .6289709}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.395604}}}{space 1}{space 1}{ralign 9:{res:{sf:      140}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:reg_east}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1925}}}{space 1}{space 1}{ralign 9:{res:{sf: .1558333}}}{space 1}{space 1}{ralign 9:{res:{sf: .3947573}}}{space 1}{space 1}{ralign 9:{res:{sf:  1.55987}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.433195}}}{space 1}{space 1}{ralign 9:{res:{sf:       77}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:reg_north}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf:     .155}}}{space 1}{space 1}{ralign 9:{res:{sf: .1313033}}}{space 1}{space 1}{ralign 9:{res:{sf: .3623579}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.906579}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.635045}}}{space 1}{space 1}{ralign 9:{res:{sf:       62}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:perc_hq}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf: 14.29691}}}{space 1}{space 1}{ralign 9:{res:{sf: 30.75215}}}{space 1}{space 1}{ralign 9:{res:{sf: 5.545462}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.537495}}}{space 1}{space 1}{ralign 9:{res:{sf: 5.618539}}}{space 1}{space 1}{ralign 9:{res:{sf: 5718.765}}}{space 1}{space 1}{ralign 9:{res:{sf: 5.766484}}}{space 1}{space 1}{ralign 9:{res:{sf: 36.42088}}}{space 1}
{space 0}{space 0}{ralign 12:perc_foreign}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf: 7.265539}}}{space 1}{space 1}{ralign 9:{res:{sf: 17.25872}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.154362}}}{space 1}{space 1}{ralign 9:{res:{sf: .5366759}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.731886}}}{space 1}{space 1}{ralign 9:{res:{sf: 2906.216}}}{space 1}{space 1}{ralign 9:{res:{sf: .8498082}}}{space 1}{space 1}{ralign 9:{res:{sf: 21.45751}}}{space 1}
{space 0}{space 0}{ralign 12:perc_female}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf: 45.81328}}}{space 1}{space 1}{ralign 9:{res:{sf: 18.96222}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.354563}}}{space 1}{space 1}{ralign 9:{res:{sf:-.2172321}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.866188}}}{space 1}{space 1}{ralign 9:{res:{sf: 18325.31}}}{space 1}{space 1}{ralign 9:{res:{sf: 29.34286}}}{space 1}{space 1}{ralign 9:{res:{sf: 58.42828}}}{space 1}
{space 0}{space 0}{ralign 12:perc_manuf~s}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf: 21.52656}}}{space 1}{space 1}{ralign 9:{res:{sf: 99.51608}}}{space 1}{space 1}{ralign 9:{res:{sf: 9.975774}}}{space 1}{space 1}{ralign 9:{res:{sf: .5265183}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.793041}}}{space 1}{space 1}{ralign 9:{res:{sf: 8610.624}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.858947}}}{space 1}{space 1}{ralign 9:{res:{sf: 56.96198}}}{space 1}
{space 0}{space 0}{ralign 12:perc_manuf~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.041149}}}{space 1}{space 1}{ralign 9:{res:{sf:  25.4111}}}{space 1}{space 1}{ralign 9:{res:{sf: 5.040943}}}{space 1}{space 1}{ralign 9:{res:{sf: 7.089951}}}{space 1}{space 1}{ralign 9:{res:{sf: 59.65266}}}{space 1}{space 1}{ralign 9:{res:{sf: 416.4595}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: 54.33518}}}{space 1}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(p1)}{space 1}{space 1}{ralign 9:e(p5)}{space 1}{space 1}{ralign 9:e(p10)}{space 1}{space 1}{ralign 9:e(p25)}{space 1}{space 1}{ralign 9:e(p50)}{space 1}{space 1}{ralign 9:e(p75)}{space 1}{space 1}{ralign 9:e(p90)}{space 1}{space 1}{ralign 9:e(p95)}{space 1}{space 1}{ralign 9:e(p99)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:afp_prop}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .0498096}}}{space 1}{space 1}{ralign 9:{res:{sf: .0697839}}}{space 1}{space 1}{ralign 9:{res:{sf: .0863305}}}{space 1}{space 1}{ralign 9:{res:{sf: .1075564}}}{space 1}{space 1}{ralign 9:{res:{sf: .1393188}}}{space 1}{space 1}{ralign 9:{res:{sf: .2072242}}}{space 1}{space 1}{ralign 9:{res:{sf: .2436473}}}{space 1}{space 1}{ralign 9:{res:{sf: .3077291}}}{space 1}
{space 0}{space 0}{ralign 12:rou_pop}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0567057}}}{space 1}{space 1}{ralign 9:{res:{sf: .0635674}}}{space 1}{space 1}{ralign 9:{res:{sf: .0672207}}}{space 1}{space 1}{ralign 9:{res:{sf: .0740081}}}{space 1}{space 1}{ralign 9:{res:{sf: .0813159}}}{space 1}{space 1}{ralign 9:{res:{sf: .0900047}}}{space 1}{space 1}{ralign 9:{res:{sf: .0990071}}}{space 1}{space 1}{ralign 9:{res:{sf: .1057745}}}{space 1}{space 1}{ralign 9:{res:{sf: .1278682}}}{space 1}
{space 0}{space 0}{ralign 12:anti_pop}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .0046362}}}{space 1}{space 1}{ralign 9:{res:{sf: .0137117}}}{space 1}{space 1}{ralign 9:{res:{sf: .0298573}}}{space 1}{space 1}{ralign 9:{res:{sf: .0557183}}}{space 1}{space 1}{ralign 9:{res:{sf: .0733382}}}{space 1}{space 1}{ralign 9:{res:{sf: .1987308}}}{space 1}
{space 0}{space 0}{ralign 12:anti}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        2}}}{space 1}{space 1}{ralign 9:{res:{sf:        5}}}{space 1}{space 1}{ralign 9:{res:{sf:        9}}}{space 1}{space 1}{ralign 9:{res:{sf:       13}}}{space 1}{space 1}{ralign 9:{res:{sf:     31.5}}}{space 1}
{space 0}{space 0}{ralign 12:reg_south}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:reg_east}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:reg_north}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:perc_hq}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 6.881177}}}{space 1}{space 1}{ralign 9:{res:{sf: 7.817706}}}{space 1}{space 1}{ralign 9:{res:{sf: 9.089828}}}{space 1}{space 1}{ralign 9:{res:{sf: 10.63613}}}{space 1}{space 1}{ralign 9:{res:{sf:  12.9793}}}{space 1}{space 1}{ralign 9:{res:{sf: 16.10734}}}{space 1}{space 1}{ralign 9:{res:{sf: 21.73898}}}{space 1}{space 1}{ralign 9:{res:{sf: 25.91532}}}{space 1}{space 1}{ralign 9:{res:{sf: 34.17693}}}{space 1}
{space 0}{space 0}{ralign 12:perc_foreign}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 1.102372}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.446724}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.990471}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.039501}}}{space 1}{space 1}{ralign 9:{res:{sf: 6.786431}}}{space 1}{space 1}{ralign 9:{res:{sf: 10.10979}}}{space 1}{space 1}{ralign 9:{res:{sf: 13.11618}}}{space 1}{space 1}{ralign 9:{res:{sf: 14.63793}}}{space 1}{space 1}{ralign 9:{res:{sf: 17.65861}}}{space 1}
{space 0}{space 0}{ralign 12:perc_female}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 33.61651}}}{space 1}{space 1}{ralign 9:{res:{sf: 38.63738}}}{space 1}{space 1}{ralign 9:{res:{sf: 40.68876}}}{space 1}{space 1}{ralign 9:{res:{sf: 43.06038}}}{space 1}{space 1}{ralign 9:{res:{sf:  45.8063}}}{space 1}{space 1}{ralign 9:{res:{sf: 48.48703}}}{space 1}{space 1}{ralign 9:{res:{sf: 51.01759}}}{space 1}{space 1}{ralign 9:{res:{sf:  53.3402}}}{space 1}{space 1}{ralign 9:{res:{sf: 55.99931}}}{space 1}
{space 0}{space 0}{ralign 12:perc_manuf~s}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 4.127999}}}{space 1}{space 1}{ralign 9:{res:{sf: 7.977122}}}{space 1}{space 1}{ralign 9:{res:{sf:  9.62051}}}{space 1}{space 1}{ralign 9:{res:{sf: 13.29267}}}{space 1}{space 1}{ralign 9:{res:{sf: 20.07836}}}{space 1}{space 1}{ralign 9:{res:{sf: 27.91664}}}{space 1}{space 1}{ralign 9:{res:{sf: 35.72295}}}{space 1}{space 1}{ralign 9:{res:{sf: 39.84246}}}{space 1}{space 1}{ralign 9:{res:{sf:  46.3646}}}{space 1}
{space 0}{space 0}{ralign 12:perc_manuf~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .0266213}}}{space 1}{space 1}{ralign 9:{res:{sf: .5361576}}}{space 1}{space 1}{ralign 9:{res:{sf: 5.190267}}}{space 1}{space 1}{ralign 9:{res:{sf: 29.84415}}}{space 1}
{com}. 
. esttab , /// 
>         cells("mean(label(Mean) fmt(2)) p50(label(Median) fmt(2)) sd(label(S.D.) fmt(2)) min(label(Min.) fmt(2)) max(label(Max) fmt(2)) count(label(Obs.) fmt(0))") ///
>         nonumber label replace noobs
{res}
{txt}{hline 98}
{txt}                                                                                                  
{txt}                             Mean       Median         S.D.         Min.          Max         Obs.
{txt}{hline 98}
{txt}AfD Share of votes  {res}         0.12         0.11         0.06         0.00         0.37          400{txt}
{txt}Share of  exposed ~s{res}         0.08         0.08         0.01         0.04         0.15          400{txt}
{txt}Hate Incidents Per~p{res}         0.02         0.01         0.03         0.00         0.36          400{txt}
{txt}\# Hate incidents ~t{res}         4.33         2.00         9.42         0.00       156.00          400{txt}
{txt}South Region        {res}         0.35         0.00         0.48         0.00         1.00          400{txt}
{txt}East Region         {res}         0.19         0.00         0.39         0.00         1.00          400{txt}
{txt}North Region        {res}         0.15         0.00         0.36         0.00         1.00          400{txt}
{txt}Employment share o~h{res}        14.30        12.98         5.55         5.77        36.42          400{txt}
{txt}Employment share o~n{res}         7.27         6.79         4.15         0.85        21.46          400{txt}
{txt}Employment share o~){res}        45.81        45.81         4.35        29.34        58.43          400{txt}
{txt}Employment sh.. (\%){res}        21.53        20.08         9.98         1.86        56.96          400{txt}
{txt}Employment sh.. of~){res}         1.04         0.00         5.04         0.00        54.34          400{txt}
{txt}{hline 98}
{com}. eststo clear
. 
. esttab using "Table\AfD_descriptive.tex", /// 
>         cells("mean(label(Mean) fmt(2)) p50(label(Median) fmt(2)) sd(label(S.D.) fmt(2)) min(label(Min.) fmt(2)) max(label(Max) fmt(2)) count(label(Obs.) fmt(0))") ///
>         nonumber label replace noobs
{res}{txt}(output written to {browse  `"Table\AfD_descriptive.tex"'})
{com}. eststo clear
. {c )-}
. {c )-}
{txt}
{com}. 
{txt}end of do-file

{com}. do "./Do/3_2_CMP_PRITM.do"
{txt}
{com}. *****************************************************************************
. * Cleaning and Analyzing - PRITM countries. Polarization proxy as distance  *
. *                                               Manifesto Project Database                          *
. *                                                                                                                                           *
. * Author:                       Valentina Gonzalez-Rostani                                                      *
. * Contact:                      mag384@pitt.edu                                                                 *
. * Date:                         March 21 2021                                                                   *
. * Version:                      Stata 17                                                                        *
. *                                                                                                                                                       *
. *****************************************************************************
. 
. /*
> This do-file:
>         A. Call the Data
>         B. Define variables
>         C. Export Tables 
> 
> Input: ** Manifesto Project database**
>         - Data\CMP\MPDataset_MPDS2020a_stata14.dta // Data download from https://manifesto-project.wzb.eu/datasets 
> 
> 
> Final output:
>         Cleaned data: 
>                 * "Data\CMP_main.dta" this data contains the relevant variables for the analysis with the DV as the polarization over redistribution, and fixed-value positions, estimated as the distance between establishment left and outsider parties.
>         Tables:
>                 * table 3: PRITM: Partisan Polarization over Redistribution and Fixed Attributes [Table\TabWithin.tex]
>                 * table A19: Partisan Polarization over Redistribution and Fixed Attributes Different Cut-Of [Table\TabWithin_cutoff.tex]
>                 * table A20: Alternative measures of Partisan Polarization over Fixed Attributes between Mainstream Left and Right-Populist [Table\TabWithin_FValternative.tex]
>                 * table A18: Descriptive statistic: PRITM 1970-2019 [Table\desc_PRITM.tex]
> 
> 
> */
. cd "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication" // Only change your directory
{res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication
{txt}
{com}. * Processing of the data (alternatively skip and go to line 809)
. {c -(}
. *##########################################
. * A. Calling the data
. *##########################################
. {c -(}
. 
. use "Data\CMP\MPDataset_MPDS2020a_stata14.dta", replace
{txt}(Manifesto Project Dataset Version 2020a. Please type "notes" for more details)
{com}. 
. {c )-}
. *############################################
. * B. Creating Variables
. *############################################
. {c -(}
. * YEAR
. gen year = year(edate) 
. 
. * Keep countries of interest
. keep if countryname=="Australia" |  countryname=="Canada" |  countryname=="Greece" |  countryname=="New Zealand" |  countryname=="Portugal" |  countryname=="Spain" |  countryname=="United Kingdom" |  countryname=="United States" |  countryname=="France" |  countryname=="Norway" | countryname=="Austria" | countryname=="Belgium" | countryname=="Denmark" | countryname=="Estonia" | countryname=="Finland" | countryname=="Hungary" | countryname=="Germany" | countryname=="Iceland"  | countryname=="Ireland"  | countryname=="Italy"  | countryname=="Netherlands"  | countryname=="Norway" | countryname=="Slovakia" | countryname=="Slovenia" | countryname=="Sweden" | countryname=="Switzerland"
{txt}(1,682 observations deleted)
{com}. 
. * Defines a dummy to identify PRITM countries
. gen PRITM=. 
{txt}(2,900 missing values generated)
{com}. replace PRITM=0 if countryname=="Australia"
{txt}(111 real changes made)
{com}. replace PRITM=0 if countryname=="Canada"
{txt}(94 real changes made)
{com}. replace PRITM=0 if countryname=="Greece"
{txt}(84 real changes made)
{com}. replace PRITM=0 if countryname=="New Zealand"
{txt}(101 real changes made)
{com}. replace PRITM=0 if countryname=="Portugal"
{txt}(101 real changes made)
{com}. replace PRITM=0 if countryname=="Spain"
{txt}(157 real changes made)
{com}. replace PRITM=0 if countryname=="United Kingdom"
{txt}(98 real changes made)
{com}. replace PRITM=0 if countryname=="United States"
{txt}(53 real changes made)
{com}. replace PRITM=0 if countryname=="France"
{txt}(116 real changes made)
{com}. 
. replace PRITM=1 if countryname=="Austria"
{txt}(83 real changes made)
{com}. replace PRITM=1 if countryname=="Belgium"
{txt}(184 real changes made)
{com}. replace PRITM=1 if countryname=="Denmark"
{txt}(235 real changes made)
{com}. replace PRITM=1 if countryname=="Estonia"
{txt}(47 real changes made)
{com}. replace PRITM=1 if countryname=="Finland"
{txt}(162 real changes made)
{com}. replace PRITM=1 if countryname=="Germany"
{txt}(89 real changes made)
{com}. replace PRITM=1 if countryname=="Hungary"
{txt}(43 real changes made)
{com}. replace PRITM=1 if countryname=="Iceland"
{txt}(117 real changes made)
{com}. replace PRITM=1 if countryname=="Ireland"
{txt}(103 real changes made)
{com}. replace PRITM=1 if countryname=="Italy"
{txt}(178 real changes made)
{com}. replace PRITM=1 if countryname=="Netherlands"
{txt}(178 real changes made)
{com}. replace PRITM=1 if countryname=="Norway"
{txt}(130 real changes made)
{com}. replace PRITM=1 if countryname=="Slovenia"
{txt}(72 real changes made)
{com}. replace PRITM=1 if countryname=="Slovakia"
{txt}(69 real changes made)
{com}. replace PRITM=1 if countryname=="Sweden"
{txt}(137 real changes made)
{com}. replace PRITM=1 if countryname=="Switzerland"
{txt}(158 real changes made)
{com}. 
. 
. * CONTROL VARIABLES - number of parties
. 
. gen number = 1
. egen number2= sum(number), by(edate)
. lab var number2 "Number of parties"
. 
. *############################################################
. * DEPENDENT VARIABLE PRITM
. *############################################################
. {c -(}
. * Obtaining relevant policy variables
. {c -(}
. gen welfare_policy =  ln(per504+0.5) - ln(per505+0.5)
{txt}(7 missing values generated)
{com}. 
. gen national_neg =  (ln(0.5 +per107)-ln(0.5 +per109)) // internationalism narrow
{txt}(7 missing values generated)
{com}. 
. gen fixed = .
{txt}(2,900 missing values generated)
{com}. replace fixed = (ln(per107+per108+per407+per602+0.5+per602_2)-ln(per109+per110+per406+per601+0.5+per601_2)) if !missing(per602_2) & !missing(per601_2)
{txt}(333 real changes made)
{com}. replace fixed = (ln(per107+per108+per407+per602+0.5)-ln(per109+per110+per406+per601+0.5)) if missing(per602_2) | missing(per601_2)
{txt}(2,560 real changes made)
{com}. 
. 
. 
. 
. gen fixed_broad_policy = .
{txt}(2,900 missing values generated)
{com}. replace fixed_broad_policy = (ln(per107+per108+per407+per602+0.5 + per602_2+per604+per607)-ln(per109+per110+per406+per601+0.5 + per601_2+per603 +per608)) if !missing(per602_2) & !missing(per601_2)
{txt}(333 real changes made)
{com}. replace fixed_broad_policy = (ln(per107+per108+per407+per602+0.5 +per604+per607)-ln(per109+per110+per406+per601+0.5 +per603 +per608)) if  missing(per602_2) | missing(per601_2)
{txt}(2,560 real changes made)
{com}. 
. gen fixed_eu_policy =  .
{txt}(2,900 missing values generated)
{com}. replace fixed_eu_policy =  (ln(per108+per602_2+0.5)-ln(per110+per601_2+0.5)) if !missing(per602_2) & !missing(per601_2)
{txt}(333 real changes made)
{com}. replace fixed_eu_policy =  (ln(per108+0.5)-ln(per110+0.5)) if  missing(per602_2) | missing(per601_2)
{txt}(2,560 real changes made)
{com}. 
. 
. gen fixed_nolog1_policy =  .
{txt}(2,900 missing values generated)
{com}. replace fixed_nolog1_policy=(per109-per107) + (per601 - per602) + (per603 - per604) + (per608 - per607) + (per601_2-per602_2) + (per406-per407)+(per110-per108) if !missing(per602_2) & !missing(per601_2)
{txt}(333 real changes made)
{com}. replace fixed_nolog1_policy=(per109-per107) + (per601 - per602) + (per603 - per604) + (per608 - per607)  + (per406-per407)+(per110-per108) if missing(per602_2) | missing(per601_2) 
{txt}(2,560 real changes made)
{com}. {c )-}
. * DEFINE PARTY FAMILY GROUPS
. {c -(}
. **  Mainstream left: - soc social democratic and socialist or other left
. gen m_left = . 
{txt}(2,900 missing values generated)
{com}. replace m_left = 1 if parfam== 30 | parfam== 20
{txt}(904 real changes made)
{com}. replace m_left = 0 if parfam ~= 30 & parfam ~=20 & parfam ~=. 
{txt}(1,996 real changes made)
{com}. 
. egen m_left_participation= max(m_left), by(edate)
. 
. 
. **  Mainstream left: only soc social democratic
. gen m_left_restrict = . 
{txt}(2,900 missing values generated)
{com}. replace m_left_restrict = 1 if parfam== 30
{txt}(577 real changes made)
{com}. replace m_left_restrict = 0 if parfam ~= 30 & parfam ~=. 
{txt}(2,323 real changes made)
{com}. 
. ** Other left: Ecologista and socialist
. gen o_left = . 
{txt}(2,900 missing values generated)
{com}. replace o_left = 1 if  parfam== 10  // here I am also including socialist or other left
{txt}(147 real changes made)
{com}. replace o_left = 0 if parfam ~= 10  & parfam ~=. 
{txt}(2,753 real changes made)
{com}. 
. egen o_left_participation= max(o_left), by(edate)
. 
. ** Mainstream right: lib liberal, Christian Democrat, Conservatives
. gen m_right = . 
{txt}(2,900 missing values generated)
{com}. replace m_right = 1 if parfam == 40 | parfam == 50 | parfam == 60
{txt}(1,174 real changes made)
{com}. replace m_right = 0 if parfam ~= 40 & parfam ~= 50 & parfam ~= 60 & parfam ~=. 
{txt}(1,726 real changes made)
{com}. 
. egen m_right_participation= max(m_right), by(edate)
. 
. ** Other right: agriculture
. gen o_right = . 
{txt}(2,900 missing values generated)
{com}. replace o_right = 1 if parfam == 80
{txt}(133 real changes made)
{com}. replace o_right = 0 if parfam ~= 80 & parfam ~=. 
{txt}(2,767 real changes made)
{com}. 
. egen o_right_participation= max(o_right), by(edate)
. 
. ** Radical right:  nat nationalist 
. gen rad_right = . 
{txt}(2,900 missing values generated)
{com}. replace rad_right = 1 if parfam == 70 
{txt}(221 real changes made)
{com}. replace rad_right = 0 if parfam ~= 70 & parfam ~=. 
{txt}(2,679 real changes made)
{com}. 
. egen rad_right_participation= max(rad_right), by(edate)
. 
. 
. ** Other parties: special issues and Ethnic and regional parties
. gen o_parties = . 
{txt}(2,900 missing values generated)
{com}. replace o_parties = 1 if parfam == 90 | parfam == 95 
{txt}(321 real changes made)
{com}. replace o_parties = 0 if parfam ~= 90 & parfam ~= 95  & parfam ~=. 
{txt}(2,579 real changes made)
{com}. 
. egen o_parties_participation= max(o_parties), by(edate)
. {c )-}
. ** DISTANCE BY PARTY FAMILY
. {c -(}
. 
. gen welfare_m_left = m_left*welfare_policy if m_left==1
{txt}(1,998 missing values generated)
{com}. gen welfare_rad_right = rad_right*welfare_policy if rad_right==1
{txt}(2,682 missing values generated)
{com}. gen welfare_m_right = m_right*welfare_policy if m_right==1
{txt}(1,727 missing values generated)
{com}. 
. gen welfare_o_left = o_left*welfare_policy if o_left==1
{txt}(2,753 missing values generated)
{com}. gen welfare_o_right = o_right*welfare_policy if o_right==1
{txt}(2,767 missing values generated)
{com}. gen welfare_o_parties = o_parties*welfare_policy if o_parties==1
{txt}(2,580 missing values generated)
{com}. 
. gen national_m_left = m_left*national_neg if m_left==1
{txt}(1,998 missing values generated)
{com}. gen national_rad_right = rad_right*national_neg if rad_right==1
{txt}(2,682 missing values generated)
{com}. gen national_m_right = m_right*national_neg if m_right==1
{txt}(1,727 missing values generated)
{com}. 
. gen fixed_m_left = m_left*fixed if m_left==1
{txt}(1,998 missing values generated)
{com}. gen fixed_rad_right = rad_right*fixed if rad_right==1
{txt}(2,682 missing values generated)
{com}. gen fixed_m_right = m_right*fixed if m_right==1
{txt}(1,727 missing values generated)
{com}. 
. gen fixed_o_left = o_left*fixed if o_left==1
{txt}(2,753 missing values generated)
{com}. gen fixed_o_right = o_right*fixed if o_right==1
{txt}(2,767 missing values generated)
{com}. gen fixed_o_parties = o_parties*fixed if o_parties==1
{txt}(2,580 missing values generated)
{com}. 
. 
. gen fixed_broad_m_left = m_left*fixed_broad_policy if m_left==1
{txt}(1,998 missing values generated)
{com}. gen fixed_broad_rad_right = rad_right*fixed_broad_policy if rad_right==1
{txt}(2,682 missing values generated)
{com}. gen fixed_broad_m_right = m_right*fixed_broad_policy if m_right==1
{txt}(1,727 missing values generated)
{com}. 
. gen fixed_eu_m_left = m_left*fixed_eu_policy if m_left==1
{txt}(1,998 missing values generated)
{com}. gen fixed_eu_rad_right = rad_right*fixed_eu_policy if rad_right==1
{txt}(2,682 missing values generated)
{com}. gen fixed_eu_m_right = m_right*fixed_eu_policy if m_right==1
{txt}(1,727 missing values generated)
{com}. 
. gen fixed_nolog1_m_left = m_left*fixed_nolog1_policy if m_left==1
{txt}(1,998 missing values generated)
{com}. gen fixed_nolog1_rad_right = rad_right*fixed_nolog1_policy if rad_right==1
{txt}(2,682 missing values generated)
{com}. gen fixed_nolog1_m_right = m_right*fixed_nolog1_policy if m_right==1
{txt}(1,727 missing values generated)
{com}. 
. 
. sort countryname edate
. 
. replace welfare_m_left = 0 if welfare_m_left ==.
{txt}(1,998 real changes made)
{com}. replace welfare_rad_right = 0 if welfare_rad_right ==.
{txt}(2,682 real changes made)
{com}. replace welfare_m_right = 0 if welfare_m_right ==.
{txt}(1,727 real changes made)
{com}. 
. replace national_m_left = 0 if national_m_left ==.
{txt}(1,998 real changes made)
{com}. replace national_rad_right = 0 if national_rad_right ==.
{txt}(2,682 real changes made)
{com}. replace national_m_right = 0 if national_m_right ==.
{txt}(1,727 real changes made)
{com}. 
. 
. replace  fixed_m_left = 0 if  fixed_m_left ==.
{txt}(1,998 real changes made)
{com}. replace  fixed_rad_right = 0 if  fixed_rad_right ==.
{txt}(2,682 real changes made)
{com}. replace  fixed_m_right = 0 if  fixed_m_right ==.
{txt}(1,727 real changes made)
{com}. 
. 
. 
. replace fixed_broad_m_left = 0 if fixed_broad_m_left ==.
{txt}(1,998 real changes made)
{com}. replace fixed_broad_rad_right = 0 if fixed_broad_rad_right ==.
{txt}(2,682 real changes made)
{com}. replace fixed_broad_m_right = 0 if fixed_broad_m_right ==.
{txt}(1,727 real changes made)
{com}. 
. replace fixed_eu_m_left = 0 if fixed_eu_m_left ==.
{txt}(1,998 real changes made)
{com}. replace fixed_eu_rad_right = 0 if fixed_eu_rad_right ==.
{txt}(2,682 real changes made)
{com}. replace fixed_eu_m_right = 0 if fixed_eu_m_right ==.
{txt}(1,727 real changes made)
{com}. 
. 
. 
. replace fixed_nolog1_m_left = 0 if fixed_nolog1_m_left ==.
{txt}(1,998 real changes made)
{com}. replace fixed_nolog1_rad_right = 0 if fixed_nolog1_rad_right ==.
{txt}(2,682 real changes made)
{com}. replace fixed_nolog1_m_right = 0 if fixed_nolog1_m_right ==.
{txt}(1,727 real changes made)
{com}. 
. 
. {c )-}       
. * Collapsing the data 
. {c -(}
. collapse (sum) fixed_m_left fixed_m_right  fixed_rad_right   welfare_m_left welfare_rad_right welfare_m_right national_m_left national_rad_right national_m_right   fixed_broad_m_left fixed_broad_rad_right fixed_broad_m_right fixed_eu_m_left fixed_eu_rad_right fixed_eu_m_right fixed_nolog1_m_left fixed_nolog1_rad_right fixed_nolog1_m_right  (first) year  PRITM  totseats number2 oecdmember date *_participation , by(edate countryname)
{res}{com}. {c )-}
. * Final prep: absolute distance
. {c -(}
. gen distance_redist = abs(welfare_m_left-welfare_m_right)
. replace distance_redist = abs(welfare_m_left-welfare_rad_right) if PRITM==1
{txt}(282 real changes made)
{com}. 
. gen distance_nat = abs(national_m_left-national_m_right)
. replace distance_nat = abs(national_m_left-national_rad_right) if PRITM==1
{txt}(268 real changes made)
{com}. 
. 
. gen distance_fixed = abs(fixed_m_left-fixed_m_right)
. replace distance_fixed = abs(fixed_m_left-fixed_rad_right) if PRITM==1
{txt}(284 real changes made)
{com}. 
. 
. 
. gen distance_fixed_all = abs(fixed_broad_m_left-fixed_broad_m_right)
. replace distance_fixed_all = abs(fixed_broad_m_left-fixed_broad_rad_right) if PRITM==1
{txt}(283 real changes made)
{com}. 
. gen distance_fixed_eu= abs(fixed_eu_m_left-fixed_eu_m_right)
. replace distance_fixed_eu = abs(fixed_eu_m_left-fixed_eu_rad_right) if PRITM==1
{txt}(231 real changes made)
{com}. 
. 
. gen distance_fixed_nolog= abs(fixed_nolog1_m_left-fixed_nolog1_m_right)
. replace distance_fixed_nolog = abs(fixed_nolog1_m_left-fixed_nolog1_rad_right) if PRITM==1
{txt}(283 real changes made)
{com}. 
. {c )-}
. 
. 
. {c )-}
. // Final Prep of the data
. {c -(}
. sort countryname edate
. 
. // Define dummy for the period pre or post 1994 when LMP was high
. gen shock=.
{txt}(474 missing values generated)
{com}. replace shock =0 if year > 1969 & year < 1995
{txt}(166 real changes made)
{com}. replace shock =1 if  year > 1994
{txt}(167 real changes made)
{com}. 
. egen country_number = group(countryname)
. 
. sort country_number edate
. bysort country_number: gen election_order=_n
. sort countryname  election_order
. 
. xtset country_number election_order
{res}
{col 1}{txt:Panel variable: }{res:country_number}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:election_order}{txt:, }{res:{bind:1}}{txt: to }{res:{bind:28}}{p_end}
{txt}{col 10}Delta: {res}1 unit
{com}.         
. {c )-}
. *############################################################
. * IFR - Alternative to the shock IV which is just  a dummy 
. *############################################################
. {c -(}
. // This includes the data provided by IFR by year and country about the number of industrial robots. Data available from 2004 to 2019   
. gen IFR=. 
{txt}(474 missing values generated)
{com}. *2004
. {c -(}
. replace IFR =0 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =123663 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =4170 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =0 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =391 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =285 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =458 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =2 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =3907 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =5987 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =120544 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =21893 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =28133 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =53244 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =2762 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =1488 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =3540 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =14176 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =2342 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =3712 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =724 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =7341 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =63 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =6 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. replace IFR =31 if countryname =="Canada" & year ==2004
{txt}(1 real change made)
{com}. {c )-}
. *2005
. {c -(}
. replace IFR =0 if countryname =="Canada" & year ==2005
{txt}(0 real changes made)
{com}. replace IFR =139984 if countryname =="United States" & year ==2005
{txt}(0 real changes made)
{com}. replace IFR =4915 if countryname =="Australia" & year ==2005
{txt}(0 real changes made)
{com}. replace IFR =23 if countryname =="New Zealand" & year ==2005
{txt}(1 real change made)
{com}. replace IFR =460 if countryname =="Slovenia" & year ==2005
{txt}(0 real changes made)
{com}. replace IFR =458 if countryname =="Hungary" & year ==2005
{txt}(0 real changes made)
{com}. replace IFR =576 if countryname =="Slovakia" & year ==2005
{txt}(0 real changes made)
{com}. replace IFR =4 if countryname =="Estonia" & year ==2005
{txt}(0 real changes made)
{com}. replace IFR =4148 if countryname =="Austria" & year ==2005
{txt}(0 real changes made)
{com}. replace IFR =6124 if countryname =="Belgium" & year ==2005
{txt}(0 real changes made)
{com}. replace IFR =126294 if countryname =="Germany" & year ==2005
{txt}(1 real change made)
{com}. replace IFR =24141 if countryname =="Spain" & year ==2005
{txt}(0 real changes made)
{com}. replace IFR =30236 if countryname =="France" & year ==2005
{txt}(0 real changes made)
{com}. replace IFR =56198 if countryname =="Italy" & year ==2005
{txt}(0 real changes made)
{com}. replace IFR =3238 if countryname =="Netherlands" & year ==2005
{txt}(0 real changes made)
{com}. replace IFR =1542 if countryname =="Portugal" & year ==2005
{txt}(1 real change made)
{com}. replace IFR =3732 if countryname =="Switzerland" & year ==2005
{txt}(0 real changes made)
{com}. replace IFR =14948 if countryname =="United Kingdom" & year ==2005
{txt}(1 real change made)
{com}. replace IFR =2661 if countryname =="Denmark" & year ==2005
{txt}(1 real change made)
{com}. replace IFR =4159 if countryname =="Finland" & year ==2005
{txt}(0 real changes made)
{com}. replace IFR =811 if countryname =="Norway" & year ==2005
{txt}(1 real change made)
{com}. replace IFR =8028 if countryname =="Sweden" & year ==2005
{txt}(0 real changes made)
{com}. replace IFR =73 if countryname =="Greece" & year ==2005
{txt}(0 real changes made)
{com}. replace IFR =7 if countryname =="Iceland" & year ==2005
{txt}(0 real changes made)
{com}. replace IFR =121 if countryname =="Ireland" & year ==2005
{txt}(0 real changes made)
{com}.         
. {c )-}
. *2006
. {c -(}
. 
. replace IFR =0 if countryname =="Canada" & year ==2006
{txt}(1 real change made)
{com}. replace IFR =150725 if countryname =="United States" & year ==2006
{txt}(0 real changes made)
{com}. replace IFR =5478 if countryname =="Australia" & year ==2006
{txt}(0 real changes made)
{com}. replace IFR =76 if countryname =="New Zealand" & year ==2006
{txt}(0 real changes made)
{com}. replace IFR =560 if countryname =="Slovenia" & year ==2006
{txt}(0 real changes made)
{com}. replace IFR =592 if countryname =="Hungary" & year ==2006
{txt}(1 real change made)
{com}. replace IFR =596 if countryname =="Slovakia" & year ==2006
{txt}(1 real change made)
{com}. replace IFR =4 if countryname =="Estonia" & year ==2006
{txt}(0 real changes made)
{com}. replace IFR =4382 if countryname =="Austria" & year ==2006
{txt}(1 real change made)
{com}. replace IFR =6331 if countryname =="Belgium" & year ==2006
{txt}(0 real changes made)
{com}. replace IFR =132594 if countryname =="Germany" & year ==2006
{txt}(0 real changes made)
{com}. replace IFR =26008 if countryname =="Spain" & year ==2006
{txt}(0 real changes made)
{com}. replace IFR =32110 if countryname =="France" & year ==2006
{txt}(0 real changes made)
{com}. replace IFR =58898 if countryname =="Italy" & year ==2006
{txt}(1 real change made)
{com}. replace IFR =3797 if countryname =="Netherlands" & year ==2006
{txt}(1 real change made)
{com}. replace IFR =1710 if countryname =="Portugal" & year ==2006
{txt}(0 real changes made)
{com}. replace IFR =3940 if countryname =="Switzerland" & year ==2006
{txt}(0 real changes made)
{com}. replace IFR =15082 if countryname =="United Kingdom" & year ==2006
{txt}(0 real changes made)
{com}. replace IFR =3013 if countryname =="Denmark" & year ==2006
{txt}(0 real changes made)
{com}. replace IFR =4349 if countryname =="Finland" & year ==2006
{txt}(0 real changes made)
{com}. replace IFR =960 if countryname =="Norway" & year ==2006
{txt}(0 real changes made)
{com}. replace IFR =8245 if countryname =="Sweden" & year ==2006
{txt}(1 real change made)
{com}. replace IFR =90 if countryname =="Greece" & year ==2006
{txt}(0 real changes made)
{com}. replace IFR =13 if countryname =="Iceland" & year ==2006
{txt}(0 real changes made)
{com}. replace IFR =191 if countryname =="Ireland" & year ==2006
{txt}(0 real changes made)
{com}.         
. {c )-}
. 
. *2007
. {c -(}
.         
. replace IFR =0 if countryname =="Canada" & year ==2007
{txt}(0 real changes made)
{com}. replace IFR =160632 if countryname =="United States" & year ==2007
{txt}(0 real changes made)
{com}. replace IFR =5998 if countryname =="Australia" & year ==2007
{txt}(1 real change made)
{com}. replace IFR =129 if countryname =="New Zealand" & year ==2007
{txt}(0 real changes made)
{com}. replace IFR =709 if countryname =="Slovenia" & year ==2007
{txt}(0 real changes made)
{com}. replace IFR =772 if countryname =="Hungary" & year ==2007
{txt}(0 real changes made)
{com}. replace IFR =677 if countryname =="Slovakia" & year ==2007
{txt}(0 real changes made)
{com}. replace IFR =6 if countryname =="Estonia" & year ==2007
{txt}(1 real change made)
{com}. replace IFR =4761 if countryname =="Austria" & year ==2007
{txt}(0 real changes made)
{com}. replace IFR =6301 if countryname =="Belgium" & year ==2007
{txt}(1 real change made)
{com}. replace IFR =139980 if countryname =="Germany" & year ==2007
{txt}(0 real changes made)
{com}. replace IFR =27473 if countryname =="Spain" & year ==2007
{txt}(0 real changes made)
{com}. replace IFR =33462 if countryname =="France" & year ==2007
{txt}(1 real change made)
{com}. replace IFR =61589 if countryname =="Italy" & year ==2007
{txt}(0 real changes made)
{com}. replace IFR =4347 if countryname =="Netherlands" & year ==2007
{txt}(0 real changes made)
{com}. replace IFR =1892 if countryname =="Portugal" & year ==2007
{txt}(0 real changes made)
{com}. replace IFR =4215 if countryname =="Switzerland" & year ==2007
{txt}(1 real change made)
{com}. replace IFR =15340 if countryname =="United Kingdom" & year ==2007
{txt}(0 real changes made)
{com}. replace IFR =3514 if countryname =="Denmark" & year ==2007
{txt}(1 real change made)
{com}. replace IFR =4495 if countryname =="Finland" & year ==2007
{txt}(1 real change made)
{com}. replace IFR =1012 if countryname =="Norway" & year ==2007
{txt}(0 real changes made)
{com}. replace IFR =8830 if countryname =="Sweden" & year ==2007
{txt}(0 real changes made)
{com}. replace IFR =144 if countryname =="Greece" & year ==2007
{txt}(1 real change made)
{com}. replace IFR =13 if countryname =="Iceland" & year ==2007
{txt}(1 real change made)
{com}. replace IFR =288 if countryname =="Ireland" & year ==2007
{txt}(1 real change made)
{com}. 
. {c )-}
. *2008
. {c -(}
.         
. replace IFR =0 if countryname =="Canada" & year ==2008
{txt}(1 real change made)
{com}. replace IFR =168489 if countryname =="United States" & year ==2008
{txt}(1 real change made)
{com}. replace IFR =6529 if countryname =="Australia" & year ==2008
{txt}(0 real changes made)
{com}. replace IFR =185 if countryname =="New Zealand" & year ==2008
{txt}(1 real change made)
{com}. replace IFR =852 if countryname =="Slovenia" & year ==2008
{txt}(1 real change made)
{com}. replace IFR =1014 if countryname =="Hungary" & year ==2008
{txt}(0 real changes made)
{com}. replace IFR =860 if countryname =="Slovakia" & year ==2008
{txt}(0 real changes made)
{com}. replace IFR =7 if countryname =="Estonia" & year ==2008
{txt}(0 real changes made)
{com}. replace IFR =5122 if countryname =="Austria" & year ==2008
{txt}(1 real change made)
{com}. replace IFR =6276 if countryname =="Belgium" & year ==2008
{txt}(0 real changes made)
{com}. replace IFR =144643 if countryname =="Germany" & year ==2008
{txt}(0 real changes made)
{com}. replace IFR =28636 if countryname =="Spain" & year ==2008
{txt}(1 real change made)
{com}. replace IFR =34370 if countryname =="France" & year ==2008
{txt}(0 real changes made)
{com}. replace IFR =63051 if countryname =="Italy" & year ==2008
{txt}(1 real change made)
{com}. replace IFR =4848 if countryname =="Netherlands" & year ==2008
{txt}(0 real changes made)
{com}. replace IFR =1990 if countryname =="Portugal" & year ==2008
{txt}(0 real changes made)
{com}. replace IFR =4431 if countryname =="Switzerland" & year ==2008
{txt}(0 real changes made)
{com}. replace IFR =15080 if countryname =="United Kingdom" & year ==2008
{txt}(0 real changes made)
{com}. replace IFR =3891 if countryname =="Denmark" & year ==2008
{txt}(0 real changes made)
{com}. replace IFR =4663 if countryname =="Finland" & year ==2008
{txt}(0 real changes made)
{com}. replace IFR =1031 if countryname =="Norway" & year ==2008
{txt}(0 real changes made)
{com}. replace IFR =9426 if countryname =="Sweden" & year ==2008
{txt}(0 real changes made)
{com}. replace IFR =203 if countryname =="Greece" & year ==2008
{txt}(0 real changes made)
{com}. replace IFR =13 if countryname =="Iceland" & year ==2008
{txt}(0 real changes made)
{com}. replace IFR =340 if countryname =="Ireland" & year ==2008
{txt}(0 real changes made)
{com}. 
. {c )-}
. *2009
. {c -(}
.         
. replace IFR =0 if countryname =="Canada" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =166183 if countryname =="United States" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =6402 if countryname =="Australia" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =295 if countryname =="New Zealand" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =903 if countryname =="Slovenia" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =1207 if countryname =="Hungary" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =1068 if countryname =="Slovakia" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =32 if countryname =="Estonia" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =5398 if countryname =="Austria" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =6448 if countryname =="Belgium" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =144133 if countryname =="Germany" & year ==2009
{txt}(1 real change made)
{com}. replace IFR =28781 if countryname =="Spain" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =34099 if countryname =="France" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =62242 if countryname =="Italy" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =5230 if countryname =="Netherlands" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =2144 if countryname =="Portugal" & year ==2009
{txt}(1 real change made)
{com}. replace IFR =4377 if countryname =="Switzerland" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =13923 if countryname =="United Kingdom" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =4076 if countryname =="Denmark" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =4719 if countryname =="Finland" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =1025 if countryname =="Norway" & year ==2009
{txt}(1 real change made)
{com}. replace IFR =9396 if countryname =="Sweden" & year ==2009
{txt}(0 real changes made)
{com}. replace IFR =242 if countryname =="Greece" & year ==2009
{txt}(1 real change made)
{com}. replace IFR =13 if countryname =="Iceland" & year ==2009
{txt}(1 real change made)
{com}. replace IFR =370 if countryname =="Ireland" & year ==2009
{txt}(0 real changes made)
{com}. 
. {c )-}
. * 2010
. {c -(}
.         
. replace IFR =0 if countryname =="Canada" & year ==2010
{txt}(0 real changes made)
{com}. replace IFR =173174 if countryname =="United States" & year ==2010
{txt}(0 real changes made)
{com}. replace IFR =6679 if countryname =="Australia" & year ==2010
{txt}(1 real change made)
{com}. replace IFR =387 if countryname =="New Zealand" & year ==2010
{txt}(0 real changes made)
{com}. replace IFR =1032 if countryname =="Slovenia" & year ==2010
{txt}(0 real changes made)
{com}. replace IFR =1406 if countryname =="Hungary" & year ==2010
{txt}(1 real change made)
{com}. replace IFR =1870 if countryname =="Slovakia" & year ==2010
{txt}(1 real change made)
{com}. replace IFR =36 if countryname =="Estonia" & year ==2010
{txt}(0 real changes made)
{com}. replace IFR =5749 if countryname =="Austria" & year ==2010
{txt}(0 real changes made)
{com}. replace IFR =6251 if countryname =="Belgium" & year ==2010
{txt}(1 real change made)
{com}. replace IFR =148256 if countryname =="Germany" & year ==2010
{txt}(0 real changes made)
{com}. replace IFR =28868 if countryname =="Spain" & year ==2010
{txt}(0 real changes made)
{com}. replace IFR =34495 if countryname =="France" & year ==2010
{txt}(0 real changes made)
{com}. replace IFR =62378 if countryname =="Italy" & year ==2010
{txt}(0 real changes made)
{com}. replace IFR =5438 if countryname =="Netherlands" & year ==2010
{txt}(1 real change made)
{com}. replace IFR =2280 if countryname =="Portugal" & year ==2010
{txt}(0 real changes made)
{com}. replace IFR =4417 if countryname =="Switzerland" & year ==2010
{txt}(0 real changes made)
{com}. replace IFR =13519 if countryname =="United Kingdom" & year ==2010
{txt}(1 real change made)
{com}. replace IFR =4234 if countryname =="Denmark" & year ==2010
{txt}(0 real changes made)
{com}. replace IFR =4611 if countryname =="Finland" & year ==2010
{txt}(0 real changes made)
{com}. replace IFR =1012 if countryname =="Norway" & year ==2010
{txt}(0 real changes made)
{com}. replace IFR =9387 if countryname =="Sweden" & year ==2010
{txt}(1 real change made)
{com}. replace IFR =286 if countryname =="Greece" & year ==2010
{txt}(0 real changes made)
{com}. replace IFR =13 if countryname =="Iceland" & year ==2010
{txt}(0 real changes made)
{com}. replace IFR =411 if countryname =="Ireland" & year ==2010
{txt}(0 real changes made)
{com}. 
. {c )-}
. *2011
. {c -(}
.         
. replace IFR =1848 if countryname =="Canada" & year ==2011
{txt}(1 real change made)
{com}. replace IFR =180893 if countryname =="United States" & year ==2011
{txt}(0 real changes made)
{com}. replace IFR =7189 if countryname =="Australia" & year ==2011
{txt}(0 real changes made)
{com}. replace IFR =510 if countryname =="New Zealand" & year ==2011
{txt}(1 real change made)
{com}. replace IFR =1194 if countryname =="Slovenia" & year ==2011
{txt}(1 real change made)
{com}. replace IFR =2347 if countryname =="Hungary" & year ==2011
{txt}(0 real changes made)
{com}. replace IFR =2210 if countryname =="Slovakia" & year ==2011
{txt}(0 real changes made)
{com}. replace IFR =47 if countryname =="Estonia" & year ==2011
{txt}(1 real change made)
{com}. replace IFR =6104 if countryname =="Austria" & year ==2011
{txt}(0 real changes made)
{com}. replace IFR =6243 if countryname =="Belgium" & year ==2011
{txt}(0 real changes made)
{com}. replace IFR =157241 if countryname =="Germany" & year ==2011
{txt}(0 real changes made)
{com}. replace IFR =29847 if countryname =="Spain" & year ==2011
{txt}(1 real change made)
{com}. replace IFR =34461 if countryname =="France" & year ==2011
{txt}(0 real changes made)
{com}. replace IFR =62245 if countryname =="Italy" & year ==2011
{txt}(0 real changes made)
{com}. replace IFR =6108 if countryname =="Netherlands" & year ==2011
{txt}(0 real changes made)
{com}. replace IFR =2372 if countryname =="Portugal" & year ==2011
{txt}(1 real change made)
{com}. replace IFR =4717 if countryname =="Switzerland" & year ==2011
{txt}(1 real change made)
{com}. replace IFR =13641 if countryname =="United Kingdom" & year ==2011
{txt}(0 real changes made)
{com}. replace IFR =4417 if countryname =="Denmark" & year ==2011
{txt}(1 real change made)
{com}. replace IFR =4473 if countryname =="Finland" & year ==2011
{txt}(1 real change made)
{com}. replace IFR =1025 if countryname =="Norway" & year ==2011
{txt}(0 real changes made)
{com}. replace IFR =9781 if countryname =="Sweden" & year ==2011
{txt}(0 real changes made)
{com}. replace IFR =290 if countryname =="Greece" & year ==2011
{txt}(0 real changes made)
{com}. replace IFR =13 if countryname =="Iceland" & year ==2011
{txt}(0 real changes made)
{com}. replace IFR =459 if countryname =="Ireland" & year ==2011
{txt}(1 real change made)
{com}. 
. {c )-}
. *2012
. {c -(}
.         
. replace IFR =3597 if countryname =="Canada" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =190321 if countryname =="United States" & year ==2012
{txt}(1 real change made)
{com}. replace IFR =7963 if countryname =="Australia" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =620 if countryname =="New Zealand" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =1474 if countryname =="Slovenia" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =3301 if countryname =="Hungary" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =2294 if countryname =="Slovakia" & year ==2012
{txt}(1 real change made)
{com}. replace IFR =51 if countryname =="Estonia" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =6619 if countryname =="Austria" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =6890 if countryname =="Belgium" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =161988 if countryname =="Germany" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =28911 if countryname =="Spain" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =33624 if countryname =="France" & year ==2012
{txt}(1 real change made)
{com}. replace IFR =60750 if countryname =="Italy" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =6718 if countryname =="Netherlands" & year ==2012
{txt}(1 real change made)
{com}. replace IFR =2524 if countryname =="Portugal" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =5010 if countryname =="Switzerland" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =15046 if countryname =="United Kingdom" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =4613 if countryname =="Denmark" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =4311 if countryname =="Finland" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =1019 if countryname =="Norway" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =9824 if countryname =="Sweden" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =311 if countryname =="Greece" & year ==2012
{txt}(2 real changes made)
{com}. replace IFR =13 if countryname =="Iceland" & year ==2012
{txt}(0 real changes made)
{com}. replace IFR =522 if countryname =="Ireland" & year ==2012
{txt}(0 real changes made)
{com}. 
. {c )-}
. *2013
. {c -(}
. 
. replace IFR =5847 if countryname =="Canada" & year ==2013
{txt}(0 real changes made)
{com}. replace IFR =203187 if countryname =="United States" & year ==2013
{txt}(0 real changes made)
{com}. replace IFR =8016 if countryname =="Australia" & year ==2013
{txt}(1 real change made)
{com}. replace IFR =770 if countryname =="New Zealand" & year ==2013
{txt}(0 real changes made)
{com}. replace IFR =1606 if countryname =="Slovenia" & year ==2013
{txt}(0 real changes made)
{com}. replace IFR =3829 if countryname =="Hungary" & year ==2013
{txt}(0 real changes made)
{com}. replace IFR =3572 if countryname =="Slovakia" & year ==2013
{txt}(0 real changes made)
{com}. replace IFR =66 if countryname =="Estonia" & year ==2013
{txt}(0 real changes made)
{com}. replace IFR =7009 if countryname =="Austria" & year ==2013
{txt}(1 real change made)
{com}. replace IFR =7998 if countryname =="Belgium" & year ==2013
{txt}(0 real changes made)
{com}. replace IFR =167579 if countryname =="Germany" & year ==2013
{txt}(1 real change made)
{com}. replace IFR =28091 if countryname =="Spain" & year ==2013
{txt}(0 real changes made)
{com}. replace IFR =32301 if countryname =="France" & year ==2013
{txt}(0 real changes made)
{com}. replace IFR =59078 if countryname =="Italy" & year ==2013
{txt}(1 real change made)
{com}. replace IFR =7403 if countryname =="Netherlands" & year ==2013
{txt}(0 real changes made)
{com}. replace IFR =2666 if countryname =="Portugal" & year ==2013
{txt}(0 real changes made)
{com}. replace IFR =5270 if countryname =="Switzerland" & year ==2013
{txt}(0 real changes made)
{com}. replace IFR =15591 if countryname =="United Kingdom" & year ==2013
{txt}(0 real changes made)
{com}. replace IFR =4760 if countryname =="Denmark" & year ==2013
{txt}(0 real changes made)
{com}. replace IFR =4268 if countryname =="Finland" & year ==2013
{txt}(0 real changes made)
{com}. replace IFR =1005 if countryname =="Norway" & year ==2013
{txt}(1 real change made)
{com}. replace IFR =10164 if countryname =="Sweden" & year ==2013
{txt}(0 real changes made)
{com}. replace IFR =344 if countryname =="Greece" & year ==2013
{txt}(0 real changes made)
{com}. replace IFR =20 if countryname =="Iceland" & year ==2013
{txt}(1 real change made)
{com}. replace IFR =605 if countryname =="Ireland" & year ==2013
{txt}(0 real changes made)
{com}.         
. {c )-}
. * 2014
. {c -(}
.         
. replace IFR =8180 if countryname =="Canada" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =219434 if countryname =="United States" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =7927 if countryname =="Australia" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =864 if countryname =="New Zealand" & year ==2014
{txt}(1 real change made)
{com}. replace IFR =1819 if countryname =="Slovenia" & year ==2014
{txt}(1 real change made)
{com}. replace IFR =4302 if countryname =="Hungary" & year ==2014
{txt}(1 real change made)
{com}. replace IFR =3891 if countryname =="Slovakia" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =83 if countryname =="Estonia" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =7237 if countryname =="Austria" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =7995 if countryname =="Belgium" & year ==2014
{txt}(1 real change made)
{com}. replace IFR =175768 if countryname =="Germany" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =27983 if countryname =="Spain" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =32233 if countryname =="France" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =59823 if countryname =="Italy" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =8470 if countryname =="Netherlands" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =2870 if countryname =="Portugal" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =5764 if countryname =="Switzerland" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =16935 if countryname =="United Kingdom" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =5119 if countryname =="Denmark" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =4178 if countryname =="Finland" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =1008 if countryname =="Norway" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =10742 if countryname =="Sweden" & year ==2014
{txt}(1 real change made)
{com}. replace IFR =392 if countryname =="Greece" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =22 if countryname =="Iceland" & year ==2014
{txt}(0 real changes made)
{com}. replace IFR =667 if countryname =="Ireland" & year ==2014
{txt}(0 real changes made)
{com}. 
. {c )-}
. * 2015
. {c -(}
.         
. replace IFR =11654 if countryname =="Canada" & year ==2015
{txt}(1 real change made)
{com}. replace IFR =234245 if countryname =="United States" & year ==2015
{txt}(0 real changes made)
{com}. replace IFR =7742 if countryname =="Australia" & year ==2015
{txt}(0 real changes made)
{com}. replace IFR =990 if countryname =="New Zealand" & year ==2015
{txt}(0 real changes made)
{com}. replace IFR =2080 if countryname =="Slovenia" & year ==2015
{txt}(0 real changes made)
{com}. replace IFR =4784 if countryname =="Hungary" & year ==2015
{txt}(0 real changes made)
{com}. replace IFR =4378 if countryname =="Slovakia" & year ==2015
{txt}(0 real changes made)
{com}. replace IFR =97 if countryname =="Estonia" & year ==2015
{txt}(1 real change made)
{com}. replace IFR =7859 if countryname =="Austria" & year ==2015
{txt}(0 real changes made)
{com}. replace IFR =7989 if countryname =="Belgium" & year ==2015
{txt}(0 real changes made)
{com}. replace IFR =182632 if countryname =="Germany" & year ==2015
{txt}(0 real changes made)
{com}. replace IFR =29718 if countryname =="Spain" & year ==2015
{txt}(1 real change made)
{com}. replace IFR =32161 if countryname =="France" & year ==2015
{txt}(0 real changes made)
{com}. replace IFR =61282 if countryname =="Italy" & year ==2015
{txt}(0 real changes made)
{com}. replace IFR =9739 if countryname =="Netherlands" & year ==2015
{txt}(0 real changes made)
{com}. replace IFR =3160 if countryname =="Portugal" & year ==2015
{txt}(1 real change made)
{com}. replace IFR =6258 if countryname =="Switzerland" & year ==2015
{txt}(1 real change made)
{com}. replace IFR =17469 if countryname =="United Kingdom" & year ==2015
{txt}(1 real change made)
{com}. replace IFR =5459 if countryname =="Denmark" & year ==2015
{txt}(1 real change made)
{com}. replace IFR =4124 if countryname =="Finland" & year ==2015
{txt}(1 real change made)
{com}. replace IFR =1068 if countryname =="Norway" & year ==2015
{txt}(0 real changes made)
{com}. replace IFR =11857 if countryname =="Sweden" & year ==2015
{txt}(0 real changes made)
{com}. replace IFR =446 if countryname =="Greece" & year ==2015
{txt}(2 real changes made)
{com}. replace IFR =23 if countryname =="Iceland" & year ==2015
{txt}(0 real changes made)
{com}. replace IFR =763 if countryname =="Ireland" & year ==2015
{txt}(0 real changes made)
{com}. 
. {c )-}
. * 2016
. {c -(}
.         
. replace IFR =13988 if countryname =="Canada" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =250479 if countryname =="United States" & year ==2016
{txt}(1 real change made)
{com}. replace IFR =7536 if countryname =="Australia" & year ==2016
{txt}(1 real change made)
{com}. replace IFR =1105 if countryname =="New Zealand" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =2452 if countryname =="Slovenia" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =5424 if countryname =="Hungary" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =6071 if countryname =="Slovakia" & year ==2016
{txt}(1 real change made)
{com}. replace IFR =123 if countryname =="Estonia" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =9000 if countryname =="Austria" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =8521 if countryname =="Belgium" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =189305 if countryname =="Germany" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =30811 if countryname =="Spain" & year ==2016
{txt}(1 real change made)
{com}. replace IFR =33384 if countryname =="France" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =62068 if countryname =="Italy" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =11320 if countryname =="Netherlands" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =3942 if countryname =="Portugal" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =6753 if countryname =="Switzerland" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =18471 if countryname =="United Kingdom" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =5915 if countryname =="Denmark" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =4422 if countryname =="Finland" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =1173 if countryname =="Norway" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =12671 if countryname =="Sweden" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =491 if countryname =="Greece" & year ==2016
{txt}(0 real changes made)
{com}. replace IFR =31 if countryname =="Iceland" & year ==2016
{txt}(1 real change made)
{com}. replace IFR =880 if countryname =="Ireland" & year ==2016
{txt}(1 real change made)
{com}. 
. {c )-}
. 
. *2017
. {c -(}
. 
. replace IFR =18045 if countryname =="Canada" & year ==2017
{txt}(0 real changes made)
{com}. replace IFR =262058 if countryname =="United States" & year ==2017
{txt}(0 real changes made)
{com}. replace IFR =7126 if countryname =="Australia" & year ==2017
{txt}(0 real changes made)
{com}. replace IFR =1172 if countryname =="New Zealand" & year ==2017
{txt}(1 real change made)
{com}. replace IFR =2805 if countryname =="Slovenia" & year ==2017
{txt}(0 real changes made)
{com}. replace IFR =7711 if countryname =="Hungary" & year ==2017
{txt}(0 real changes made)
{com}. replace IFR =7093 if countryname =="Slovakia" & year ==2017
{txt}(0 real changes made)
{com}. replace IFR =170 if countryname =="Estonia" & year ==2017
{txt}(0 real changes made)
{com}. replace IFR =10156 if countryname =="Austria" & year ==2017
{txt}(1 real change made)
{com}. replace IFR =9207 if countryname =="Belgium" & year ==2017
{txt}(0 real changes made)
{com}. replace IFR =200497 if countryname =="Germany" & year ==2017
{txt}(1 real change made)
{com}. replace IFR =32352 if countryname =="Spain" & year ==2017
{txt}(0 real changes made)
{com}. replace IFR =35321 if countryname =="France" & year ==2017
{txt}(1 real change made)
{com}. replace IFR =64403 if countryname =="Italy" & year ==2017
{txt}(0 real changes made)
{com}. replace IFR =12505 if countryname =="Netherlands" & year ==2017
{txt}(1 real change made)
{com}. replace IFR =4622 if countryname =="Portugal" & year ==2017
{txt}(0 real changes made)
{com}. replace IFR =7476 if countryname =="Switzerland" & year ==2017
{txt}(0 real changes made)
{com}. replace IFR =19488 if countryname =="United Kingdom" & year ==2017
{txt}(1 real change made)
{com}. replace IFR =6361 if countryname =="Denmark" & year ==2017
{txt}(0 real changes made)
{com}. replace IFR =4342 if countryname =="Finland" & year ==2017
{txt}(0 real changes made)
{com}. replace IFR =1250 if countryname =="Norway" & year ==2017
{txt}(1 real change made)
{com}. replace IFR =13249 if countryname =="Sweden" & year ==2017
{txt}(0 real changes made)
{com}. replace IFR =568 if countryname =="Greece" & year ==2017
{txt}(0 real changes made)
{com}. replace IFR =42 if countryname =="Iceland" & year ==2017
{txt}(1 real change made)
{com}. replace IFR =945 if countryname =="Ireland" & year ==2017
{txt}(0 real changes made)
{com}.         
. {c )-}
. *2018
. {c -(}
. 
. replace IFR =21627 if countryname =="Canada" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =285014 if countryname =="United States" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =6927 if countryname =="Australia" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =1220 if countryname =="New Zealand" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =3414 if countryname =="Slovenia" & year ==2018
{txt}(1 real change made)
{com}. replace IFR =8481 if countryname =="Hungary" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =7796 if countryname =="Slovakia" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =220 if countryname =="Estonia" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =11162 if countryname =="Austria" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =9561 if countryname =="Belgium" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =215795 if countryname =="Germany" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =35209 if countryname =="Spain" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =38079 if countryname =="France" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =69142 if countryname =="Italy" & year ==2018
{txt}(1 real change made)
{com}. replace IFR =13385 if countryname =="Netherlands" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =5050 if countryname =="Portugal" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =8492 if countryname =="Switzerland" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =20683 if countryname =="United Kingdom" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =6617 if countryname =="Denmark" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =4553 if countryname =="Finland" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =1219 if countryname =="Norway" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =13647 if countryname =="Sweden" & year ==2018
{txt}(1 real change made)
{com}. replace IFR =640 if countryname =="Greece" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =37 if countryname =="Iceland" & year ==2018
{txt}(0 real changes made)
{com}. replace IFR =1026 if countryname =="Ireland" & year ==2018
{txt}(0 real changes made)
{com}. 
.         
. {c )-}
. * 2019
. {c -(}
. replace IFR =25230 if countryname =="Canada" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =299674 if countryname =="United States" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =6649 if countryname =="Australia" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =1278 if countryname =="New Zealand" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =3941 if countryname =="Slovenia" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =9212 if countryname =="Hungary" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =8326 if countryname =="Slovakia" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =283 if countryname =="Estonia" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =12016 if countryname =="Austria" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =10109 if countryname =="Belgium" & year ==2019
{txt}(1 real change made)
{com}. replace IFR =223387 if countryname =="Germany" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =36916 if countryname =="Spain" & year ==2019
{txt}(1 real change made)
{com}. replace IFR =42054 if countryname =="France" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =74420 if countryname =="Italy" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =14370 if countryname =="Netherlands" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =5620 if countryname =="Portugal" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =9506 if countryname =="Switzerland" & year ==2019
{txt}(1 real change made)
{com}. replace IFR =21678 if countryname =="United Kingdom" & year ==2019
{txt}(1 real change made)
{com}. replace IFR =6824 if countryname =="Denmark" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =4728 if countryname =="Finland" & year ==2019
{txt}(1 real change made)
{com}. replace IFR =1271 if countryname =="Norway" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =14224 if countryname =="Sweden" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =665 if countryname =="Greece" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =37 if countryname =="Iceland" & year ==2019
{txt}(0 real changes made)
{com}. replace IFR =1130 if countryname =="Ireland" & year ==2019
{txt}(0 real changes made)
{com}.         
. {c )-}
. 
. // Now the log of the number of industrial robots
. gen IFR2 = ln(IFR)
{txt}(374 missing values generated)
{com}. {c )-}
. {c )-}
. 
. *############################################
. * Saving the data
. *############################################
. {c -(}
. lab var IFR2 "Robots Stock"
. lab var PRITM "PR with Trichotomous Multipartism"
. lab var totseats "Total Number of Seats"
. lab var number2 "Total Number of Parties"
. lab var oecdmember "OECD member"
. lab var distance_redist "Distance Redistribution (DR) - Net Welfare"
. lab var distance_fixed "Distance Fixed-Value Positions (DFVP) - Net Anti-Global"
. lab var IFR2 "Robots Stock"
. 
. lab var distance_fixed_eu "DFVP - Net Anti-EU"
. lab var distance_nat "DFVP - Net Anti-Global Narrow (Internationalism)"
. lab var distance_fixed_all "DFVP - Anti-Global and Cultural"
. 
. lab var shock "High LMP period"
. 
. keep  PRITM year  countryname  oecdmember totseats shock distance_redist distance_fixed  distance_fixed_eu  distance_nat  distance_fixed_all distance_fixed_nolog IFR IFR2 number2 election_order country_number
. 
. keep if year>1969
{txt}(141 observations deleted)
{com}. keep if PRITM==1
{txt}(131 observations deleted)
{com}. 
. save "Data\CMP_main.dta", replace
{txt}{p 0 4 2}
file {bf}
Data\CMP_main.dta{rm}
saved
{p_end}
{com}. {c )-}
. {c )-}
{txt}
{com}. * Alternatively load prepared data
. {c -(}
. use "Data\CMP_main.dta", clear  
{txt}(Manifesto Project Dataset Version 2020a. Please type "notes" for more details)
{com}. {c )-}
{txt}
{com}. *##########################################
. * Analysis 
. *##########################################
. {c -(}
. // table 3: PRITM: Partisan Polarization over Redistribution and Fixed Attributes
. {c -(}
. preserve
. 
. eststo clear
. eststo: qui reg distance_redist L.distance_redist shock totseats   oecdmember   i.year, cluster(countryname)
{txt}({res}est1{txt} stored)
{com}. eststo: qui reg distance_fixed L.distance_fixed shock totseats  oecdmember   i.year, cluster(countryname)
{txt}({res}est2{txt} stored)
{com}. eststo: qui reg distance_redist L.distance_redist IFR2 totseats  oecdmember   i.year, cluster(countryname)
{txt}({res}est3{txt} stored)
{com}. eststo: qui reg distance_fixed L.distance_fixed IFR2 totseats  oecdmember   i.year, cluster(countryname)
{txt}({res}est4{txt} stored)
{com}. 
. 
. 
. esttab , replace label se title(Polarization over Redistribution and Fixed Attributes between Mainstream Left and Right-Populist \label {c -(}TableCMPAllFE{c )-}) mti("Redistribution" "Fixed Values" "Redistribution" "Fixed Values" ) compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(shock    IFR2 ) scalars(N r2 aic) indicate("LDV = L.*" "FE Year = *year") 
{res}
{txt}Polarization over Redistribution and Fixed Attributes between Mainstream Left and Right-Populist \label {TableCMPAllFE}
{txt}{hline 68}
{txt}                       (1)          (2)          (3)          (4)   
{txt}                 Redistr~n    Fixed V~s    Redistr~n    Fixed V~s   
{txt}{hline 68}
{txt}High LMP period {res}     2.919        2.708***                          {txt}
                {res} {ralign 9:{txt:(}2.304{txt:)}}    {ralign 9:{txt:(}0.866{txt:)}}                             {txt}
{txt}Robots Stock    {res}                               0.074        0.561** {txt}
                {res}                           {ralign 9:{txt:(}0.215{txt:)}}    {ralign 9:{txt:(}0.219{txt:)}}   {txt}
{txt}LDV             {res}       Yes          Yes          Yes          Yes   {txt}
{txt}FE Year         {res}       Yes          Yes          Yes          Yes   {txt}
{txt}{hline 68}
{txt}Observations    {res}       186          186           62           62   {txt}
{txt}r2              {res}     0.519        0.333        0.492        0.372   {txt}
{txt}aic             {res}   783.599      768.187      291.086      285.773   {txt}
{txt}{hline 68}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}. 
. esttab using "Table\TabWithin.tex", replace label se title(Polarization over Redistribution and Fixed Attributes  \label {c -(}TableCMPAll{c )-}) mti("Redistribution" "Fixed Values" "Redistribution" "Fixed Values") compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(shock  IFR2) scalars(N r2 aic) indicate("LDV = L.*" "FE Year = *year")
{res}{txt}(output written to {browse  `"Table\TabWithin.tex"'})
{com}. 
. restore
. 
. {c )-}
. // table A19: Partisan Polarization over Redistribution and Fixed Attributes Different Cut-Of
. {c -(}
. gen shock92=.
{txt}(202 missing values generated)
{com}. replace shock92 =0 if year > 1969 & year < 1993
{txt}(91 real changes made)
{com}. replace shock92 =1 if  year > 1992
{txt}(111 real changes made)
{com}. 
. gen shock93=.
{txt}(202 missing values generated)
{com}. replace shock93 =0 if year > 1969 & year < 1994
{txt}(92 real changes made)
{com}. replace shock93 =1 if  year > 1993
{txt}(110 real changes made)
{com}. 
. gen shock96=.
{txt}(202 missing values generated)
{com}. replace shock96 =0 if year > 1969 & year < 1996
{txt}(106 real changes made)
{com}. replace shock96 =1 if  year > 1997
{txt}(92 real changes made)
{com}. 
. gen shock97=.
{txt}(202 missing values generated)
{com}. replace shock97 =0 if year > 1969 & year < 1997
{txt}(108 real changes made)
{com}. replace shock97 =1 if  year > 1998
{txt}(86 real changes made)
{com}. 
. gen shock98=.
{txt}(202 missing values generated)
{com}. replace shock98 =0 if year > 1969 & year < 1998
{txt}(110 real changes made)
{com}. replace shock98 =1 if  year > 1999
{txt}(80 real changes made)
{com}. 
. 
. lab var shock98 "Post-LMP"
. lab var shock92 "Post-LMP"
. lab var shock93 "Post-LMP"
. lab var shock96 "Post-LMP"
. lab var shock97 "Post-LMP"
. 
. 
. preserve
.   
. eststo clear
. eststo: qui reg distance_redist L.distance_redist shock92 totseats  oecdmember   i.year, cluster(countryname)
{txt}({res}est1{txt} stored)
{com}. eststo: qui reg distance_fixed L.distance_fixed shock92 totseats  oecdmember   i.year, cluster(countryname)
{txt}({res}est2{txt} stored)
{com}. 
. eststo: qui reg distance_redist L.distance_redist shock93 totseats  oecdmember   i.year, cluster(countryname)
{txt}({res}est3{txt} stored)
{com}. eststo: qui reg distance_fixed L.distance_fixed shock93 totseats  oecdmember   i.year, cluster(countryname)
{txt}({res}est4{txt} stored)
{com}. 
. 
. eststo: qui reg distance_redist L.distance_redist shock96 totseats  oecdmember   i.year, cluster(countryname)
{txt}({res}est5{txt} stored)
{com}. eststo: qui reg distance_fixed L.distance_fixed shock96 totseats  oecdmember   i.year, cluster(countryname)
{txt}({res}est6{txt} stored)
{com}. 
. 
. eststo: qui reg distance_redist L.distance_redist shock97 totseats  oecdmember   i.year, cluster(countryname)
{txt}({res}est7{txt} stored)
{com}. eststo: qui reg distance_fixed L.distance_fixed shock97 totseats  oecdmember   i.year, cluster(countryname)
{txt}({res}est8{txt} stored)
{com}. 
. 
. eststo: qui reg distance_redist L.distance_redist shock98 totseats  oecdmember   i.year, cluster(countryname)
{txt}({res}est9{txt} stored)
{com}. eststo: qui reg distance_fixed L.distance_fixed shock98 totseats  oecdmember   i.year, cluster(countryname)
{txt}({res}est10{txt} stored)
{com}. 
. esttab , replace label se title(Partisan Polarization over Redistribution and Fixed Attributes Different Cut-Off \label {c -(}Tablewithincut{c )-}) mti("Redist" "Fixed" "Redist" "Fixed" "Redist" "Fixed" "Redist" "Fixed" "Redist" "Fixed" ) compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(shock*     ) scalars( "N Observations" "r2 R$^2$" "aic AIC" )  indicate("LDV = L.*" "FE Year = *year")
{res}
{txt}Partisan Polarization over Redistribution and Fixed Attributes Different Cut-Off \label {Tablewithincut}
{txt}{hline 146}
{txt}                       (1)          (2)          (3)          (4)          (5)          (6)          (7)          (8)          (9)         (10)   
{txt}                    Redist        Fixed       Redist        Fixed       Redist        Fixed       Redist        Fixed       Redist        Fixed   
{txt}{hline 146}
{txt}Post-LMP        {res}     2.919        2.708***                                                                                                        {txt}
                {res} {ralign 9:{txt:(}2.304{txt:)}}    {ralign 9:{txt:(}0.866{txt:)}}                                                                                                           {txt}
{txt}Post-LMP        {res}                               2.919        2.708***                                                                              {txt}
                {res}                           {ralign 9:{txt:(}2.304{txt:)}}    {ralign 9:{txt:(}0.866{txt:)}}                                                                                 {txt}
{txt}Post-LMP        {res}                                                         2.921        2.679***                                                    {txt}
                {res}                                                     {ralign 9:{txt:(}2.297{txt:)}}    {ralign 9:{txt:(}0.855{txt:)}}                                                       {txt}
{txt}Post-LMP        {res}                                                                                   2.791        2.696***                          {txt}
                {res}                                                                               {ralign 9:{txt:(}2.269{txt:)}}    {ralign 9:{txt:(}0.864{txt:)}}                             {txt}
{txt}Post-LMP        {res}                                                                                                             2.642        2.671***{txt}
                {res}                                                                                                         {ralign 9:{txt:(}2.240{txt:)}}    {ralign 9:{txt:(}0.838{txt:)}}   {txt}
{txt}LDV             {res}       Yes          Yes          Yes          Yes          Yes          Yes          Yes          Yes          Yes          Yes   {txt}
{txt}FE Year         {res}       Yes          Yes          Yes          Yes          Yes          Yes          Yes          Yes          Yes          Yes   {txt}
{txt}{hline 146}
{txt}Observations    {res}       186          186          186          186          182          182          178          178          174          174   {txt}
{txt}R$^2$           {res}     0.519        0.333        0.519        0.333        0.516        0.344        0.531        0.352        0.542        0.360   {txt}
{txt}AIC             {res}   783.599      768.187      783.599      768.187      771.187      752.039      751.737      733.099      733.090      709.586   {txt}
{txt}{hline 146}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}. 
. esttab using "Table\TabWithin_cutoff.tex", replace label se title(Partisan Polarization over Redistribution and Fixed Attributes Different Cut-Off \label {c -(}Tablewithincut{c )-}) mti("Redist" "Fixed" "Redist" "Fixed" "Redist" "Fixed" "Redist" "Fixed" "Redist" "Fixed" ) compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(shock*     ) scalars( "N Observations" "r2 R$^2$" "aic AIC" )  indicate("LDV = L.*" "FE Year = *year")
{res}{txt}(output written to {browse  `"Table\TabWithin_cutoff.tex"'})
{com}. 
. restore 
. {c )-}
. // table A20: Alternative measures of Partisan Polarization over Fixed Attributes between Mainstream Left and Right-Populist
. {c -(}
.         lab var distance_fixed_eu "Anti-EU"
. lab var distance_nat "Internationalism"
. lab var distance_fixed_all "Anti-Global and Cultural"
. lab var distance_fixed_nolog "Anti-Global and Cultural (no log)"
. 
. preserve
. 
. eststo clear
. 
. foreach x of varlist distance_fixed_eu  distance_nat  distance_fixed_all distance_fixed_nolog {c -(}
{txt}  2{com}. eststo: qui reg `x' L.`x' shock totseats  oecdmember   i.year, cluster(countryname)
{txt}  3{com}. eststo: qui reg `x' L.`x' IFR2 totseats  oecdmember   i.year, cluster(countryname)
{txt}  4{com}. 
. {c )-}
{txt}({res}est1{txt} stored)
({res}est2{txt} stored)
({res}est3{txt} stored)
({res}est4{txt} stored)
({res}est5{txt} stored)
({res}est6{txt} stored)
({res}est7{txt} stored)
({res}est8{txt} stored)
{com}. esttab, replace label se title(Alternative measures of Partisan Polarization over Fixed Attributes between Mainstream Left and Right-Populist \label {c -(}FVPritm{c )-})  compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(shock IFR2 ) scalars( "N Observations" "r2 R$^2$" "aic AIC" ) indicate("Controls = tot*" "LDV = L.*" "FE Year = *year") 
{res}
{txt}Alternative measures of Partisan Polarization over Fixed Attributes between Mainstream Left and Right-Populist \label {FVPritm}
{txt}{hline 120}
{txt}                       (1)          (2)          (3)          (4)          (5)          (6)          (7)          (8)   
{txt}                   Anti-EU      Anti-EU    Interna~m    Interna~m    Anti-Gl~l    Anti-Gl~l    Anti-Gl~g    Anti-Gl~g   
{txt}{hline 120}
{txt}High LMP period {res}     2.581***                  1.491***                  3.603***                 21.639***             {txt}
                {res} {ralign 9:{txt:(}0.362{txt:)}}                 {ralign 9:{txt:(}0.450{txt:)}}                 {ralign 9:{txt:(}0.571{txt:)}}                 {ralign 9:{txt:(}5.703{txt:)}}                {txt}
{txt}Robots Stock    {res}                  0.306**                   0.411**                   0.589***                  5.678***{txt}
                {res}              {ralign 9:{txt:(}0.125{txt:)}}                 {ralign 9:{txt:(}0.167{txt:)}}                 {ralign 9:{txt:(}0.177{txt:)}}                 {ralign 9:{txt:(}1.714{txt:)}}   {txt}
{txt}Controls        {res}       Yes          Yes          Yes          Yes          Yes          Yes          Yes          Yes   {txt}
{txt}LDV             {res}       Yes          Yes          Yes          Yes          Yes          Yes          Yes          Yes   {txt}
{txt}FE Year         {res}       Yes          Yes          Yes          Yes          Yes          Yes          Yes          Yes   {txt}
{txt}{hline 120}
{txt}Observations    {res}       186           62          186           62          186           62          186           62   {txt}
{txt}R$^2$           {res}     0.532        0.472        0.373        0.363        0.408        0.467        0.614        0.552   {txt}
{txt}AIC             {res}   666.836      249.215      699.495      258.617      787.820      278.176     1509.478      534.144   {txt}
{txt}{hline 120}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}. 
. esttab using "Table\TabWithin_FValternative.tex", replace label se title(Alternative measures of Partisan Polarization over Fixed Attributes between Mainstream Left and Right-Populist \label {c -(}FVPritm{c )-})  compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(shock IFR2 ) scalars( "N Observations" "r2 R$^2$" "aic AIC" ) indicate("Controls = tot*" "LDV = L.*" "FE Year = *year") 
{res}{txt}(output written to {browse  `"Table\TabWithin_FValternative.tex"'})
{com}. 
. restore
. 
. {c )-}
. {c )-}
{txt}
{com}. *##########################################
. * Descriptive 
. *##########################################
. // table A18: Descriptive statistic: PRITM 1970-2019
. {c -(}
.         
. lab var distance_fixed_eu "DFVP - Net Anti-EU"
. lab var distance_nat "DFVP - Net Anti-Global Narrow (Internationalism)"
. lab var distance_fixed_all "DFVP - Anti-Global and Cultural"
. lab var distance_fixed_nolog "DFVP - Anti-Global and Cultural (no log)"
.         
. lab var IFR "\# Robot Stock (IFR)"
. lab var IFR2 "Ln \# Robot Stock (IFR)"
. lab var distance_fixed_nolog "DFVP - Anti-Global and Cultural (no log)"
. preserve
. 
.         eststo clear
. 
. qui estpost sum  totseats number2 oecdmember distance_redist  distance_fixed  distance_fixed_eu   distance_fixed_all distance_nat  distance_fixed_all distance_fixed_nolog IFR IFR2, d 
. 
. 
. esttab ,cells("mean(label(Mean) fmt(2)) p50(label(Median) fmt(2)) sd(label(S.D.) fmt(2)) min(label(Min.) fmt(0)) max(label(Max) fmt(0)) count(label(Obs.) fmt(0))") ///
>         nonumber label replace noobs  title(Descriptive statistic: PRITM 1970-2019 \label {c -(}SummarystatPRITM{c )-}) 
{res}
{txt}Descriptive statistic: PRITM 1970-2019 \label {SummarystatPRITM}
{txt}{hline 98}
{txt}                                                                                                  
{txt}                             Mean       Median         S.D.         Min.          Max         Obs.
{txt}{hline 98}
{txt}Total Number of Se~s{res}       235.44       175.00       162.72           60          709          202{txt}
{txt}Total Number of Pa~s{res}         7.72         8.00         2.62            3           19          202{txt}
{txt}OECD member         {res}         9.16        10.00         2.78            0           10          202{txt}
{txt}Distance Redistrib~N{res}         4.46         4.02         2.65            0           17          202{txt}
{txt}Distance Fixed-Val~({res}         3.07         2.84         2.12            0           12          202{txt}
{txt}DFVP - Net Anti-EU  {res}         2.19         1.82         1.93            0            9          202{txt}
{txt}DFVP - Anti-Global~l{res}         3.18         2.62         2.37            0           13          202{txt}
{txt}DFVP - Net Anti-Gl~I{res}         2.63         2.50         1.83            0            9          202{txt}
{txt}DFVP - Anti-Global~l{res}         3.18         2.62         2.37            0           13          202{txt}
{txt}DFVP - Anti-Global~ {res}        18.59        12.11        20.42            0          110          202{txt}
{txt}\# Robot Stock (IFR){res}     17829.68      4399.50     41004.76            6       200497           62{txt}
{txt}Ln \# Robot Stock ~){res}         7.90         8.39         2.34            2           12           62{txt}
{txt}{hline 98}
{com}.         
. esttab using "Table\desc_PRITM.tex",cells("mean(label(Mean) fmt(2)) p50(label(Median) fmt(2)) sd(label(S.D.) fmt(2)) min(label(Min.) fmt(0)) max(label(Max) fmt(0)) count(label(Obs.) fmt(0))") ///
>         nonumber label replace noobs  title(Descriptive statistic: PRITM 1970-2019 \label {c -(}SummarystatPRITM{c )-}) 
{res}{txt}(output written to {browse  `"Table\desc_PRITM.tex"'})
{com}.         
.         restore
.         {c )-}
{txt}
{com}. 
{txt}end of do-file

{com}. do "./Do/3_3_CMP_PRITM_Appendix_Average.do"
{txt}
{com}. *****************************************************************************
. * Cleaning and Analyzing - PRITM countries. Alternative proxy                           *
. *   . Polarization proxy as Average distance in all the party system        *
. *                                               Manifesto Project Database                          *
. *                                                                                                                                           *
. * Author:                       Valentina Gonzalez-Rostani                                                      *
. * Contact:                      mag384@pitt.edu                                                                 *
. * Date:                         March 21 2021                                                                           *
. * Version:                      Stata 17                                                                        *
. *                                                                                                                                                       *
. *****************************************************************************
. 
. /*
> This do-file:
>         A. Call the Data
>         B. Define variables
>         C. Export Tables 
> 
> Input: ** Manifesto Project database**
>         - Data\CMP\MPDataset_MPDS2020a_stata14.dta // Data download from https://manifesto-project.wzb.eu/datasets 2021
> 
> 
> Final output:
>         Cleaned data: 
>                 * "Data\CMP_average.dta" this data contains the relevant variables for the analysis with the DV as the polarization over redistribution, and fixed-value positions, estimated as the average distance of the party system.
>         Tables:
>                 * table A21: Partisan Polarization over Redistribution and Fixed Attributes
> 
> 
> */
. 
. cd "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication" // Only change your directory
{res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication
{txt}
{com}. 
. * Processing of the data (alternatively skip and go to line 158)
. {c -(}
. *##########################################
. * A. Calling the data
. *##########################################
. {c -(}
. 
. use "Data\CMP\MPDataset_MPDS2020a_stata14.dta", replace
{txt}(Manifesto Project Dataset Version 2020a. Please type "notes" for more details)
{com}. 
. {c )-}
. *############################################
. * B. Creating Variables
. *############################################
. {c -(}
. * YEAR
. gen year = year(edate) 
. 
. * Keep countries of interest
. keep if countryname=="Australia" |  countryname=="Canada" |  countryname=="Greece" |  countryname=="New Zealand" |  countryname=="Portugal" |  countryname=="Spain" |  countryname=="United Kingdom" |  countryname=="United States" |  countryname=="France" |  countryname=="Norway" | countryname=="Austria" | countryname=="Belgium" | countryname=="Denmark" | countryname=="Estonia" | countryname=="Finland" | countryname=="Hungary" | countryname=="Germany" | countryname=="Iceland"  | countryname=="Ireland"  | countryname=="Italy"  | countryname=="Netherlands"  | countryname=="Norway" | countryname=="Slovakia" | countryname=="Slovenia" | countryname=="Sweden" | countryname=="Switzerland"
{txt}(1,682 observations deleted)
{com}. 
. * Defines a dummy to identify PRITM countries
. gen PRITM=. 
{txt}(2,900 missing values generated)
{com}. replace PRITM=0 if countryname=="Australia"
{txt}(111 real changes made)
{com}. replace PRITM=0 if countryname=="Canada"
{txt}(94 real changes made)
{com}. replace PRITM=0 if countryname=="Greece"
{txt}(84 real changes made)
{com}. replace PRITM=0 if countryname=="New Zealand"
{txt}(101 real changes made)
{com}. replace PRITM=0 if countryname=="Portugal"
{txt}(101 real changes made)
{com}. replace PRITM=0 if countryname=="Spain"
{txt}(157 real changes made)
{com}. replace PRITM=0 if countryname=="United Kingdom"
{txt}(98 real changes made)
{com}. replace PRITM=0 if countryname=="United States"
{txt}(53 real changes made)
{com}. replace PRITM=0 if countryname=="France"
{txt}(116 real changes made)
{com}. 
. replace PRITM=1 if countryname=="Austria"
{txt}(83 real changes made)
{com}. replace PRITM=1 if countryname=="Belgium"
{txt}(184 real changes made)
{com}. replace PRITM=1 if countryname=="Denmark"
{txt}(235 real changes made)
{com}. replace PRITM=1 if countryname=="Estonia"
{txt}(47 real changes made)
{com}. replace PRITM=1 if countryname=="Finland"
{txt}(162 real changes made)
{com}. replace PRITM=1 if countryname=="Germany"
{txt}(89 real changes made)
{com}. replace PRITM=1 if countryname=="Hungary"
{txt}(43 real changes made)
{com}. replace PRITM=1 if countryname=="Iceland"
{txt}(117 real changes made)
{com}. replace PRITM=1 if countryname=="Ireland"
{txt}(103 real changes made)
{com}. replace PRITM=1 if countryname=="Italy"
{txt}(178 real changes made)
{com}. replace PRITM=1 if countryname=="Netherlands"
{txt}(178 real changes made)
{com}. replace PRITM=1 if countryname=="Norway"
{txt}(130 real changes made)
{com}. replace PRITM=1 if countryname=="Slovenia"
{txt}(72 real changes made)
{com}. replace PRITM=1 if countryname=="Slovakia"
{txt}(69 real changes made)
{com}. replace PRITM=1 if countryname=="Sweden"
{txt}(137 real changes made)
{com}. replace PRITM=1 if countryname=="Switzerland"
{txt}(158 real changes made)
{com}. 
. 
. * CONTROL VARIABLES - number of parties
. 
. gen number = 1
. egen number2= sum(number), by(edate)
. lab var number2 "Number of parties"
. 
. *############################################################
. * DEPENDENT VARIABLE Redistribution and Fixed Attributes
. *############################################################
. {c -(}
. * Obtaining relevant policy variables
. 
. gen welfare_policy =  ln(per504+0.5) - ln(per505+0.5)
{txt}(7 missing values generated)
{com}. 
. gen fixed = .
{txt}(2,900 missing values generated)
{com}. replace fixed = (ln(per107+per108+per407+per602+0.5+per602_2)-ln(per109+per110+per406+per601+0.5+per601_2)) if !missing(per602_2) & !missing(per601_2)
{txt}(333 real changes made)
{com}. replace fixed = (ln(per107+per108+per407+per602+0.5)-ln(per109+per110+per406+per601+0.5)) if missing(per602_2) | missing(per601_2)
{txt}(2,560 real changes made)
{com}. 
. {c )-}
. 
. ** Different code compared to 3_2 do file: 
. ***** Now average distance instead of distance between certain party families
. {c -(}
. egen av_welfare_policy= mean(welfare_policy), by(edate countryname)
. gen dist_av_welfare_policy = abs(welfare_policy-av_welfare_policy)
{txt}(7 missing values generated)
{com}. 
. egen av_fixed= mean(fixed), by(edate countryname)
. gen dist_av_fixed = abs(fixed-av_fixed)
{txt}(7 missing values generated)
{com}. 
. {c )-}
. * Collapsing the data 
. {c -(}
. collapse (sum) av_welfare_policy dist_av_welfare_policy   av_fixed dist_av_fixed  (first) year  PRITM  totseats number2 oecdmember date  , by(edate countryname)
{res}{com}. {c )-}
. // Final Prep of the data
. {c -(}
. egen country_number = group(countryname)
. sort country_number edate
. bysort country_number: gen election_order=_n
. xtset country_number election_order
{res}
{col 1}{txt:Panel variable: }{res:country_number}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:election_order}{txt:, }{res:{bind:1}}{txt: to }{res:{bind:28}}{p_end}
{txt}{col 10}Delta: {res}1 unit
{com}. sort countryname  election_order
. sort countryname  year
. 
. gen shock=.
{txt}(474 missing values generated)
{com}. replace shock =0 if year > 1969 & year < 1995
{txt}(166 real changes made)
{com}. replace shock =1 if  year > 1994
{txt}(167 real changes made)
{com}. xtset country_number election_order
{res}
{col 1}{txt:Panel variable: }{res:country_number}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:election_order}{txt:, }{res:{bind:1}}{txt: to }{res:{bind:28}}{p_end}
{txt}{col 10}Delta: {res}1 unit
{com}. {c )-}
. {c )-}
. *############################################
. * Saving the data
. *############################################
. {c -(}
. 
. lab var PRITM "PR with Trichotomous Multipartism"
. lab var totseats "Total Number of Seats"
. lab var number2 "Total Number of Parties"
. lab var oecdmember "OECD member"
. lab var PRITM "PRITM"
. 
. lab var dist_av_welfare_policy "Distance Redistribution (DR) - Net Welfare"
. lab var dist_av_fixed "Distance Fixed-Value Positions (DFVP) - Net Anti-Global"
. 
. lab var shock "High LMP period" // post 1994
. 
. keep  PRITM year  countryname  oecdmember totseats shock dist_av_welfare_policy dist_av_fixed   number2 election_order country_number
. 
. keep if year>1969
{txt}(141 observations deleted)
{com}. keep if PRITM==1
{txt}(131 observations deleted)
{com}. 
. save "Data\CMP_average.dta", replace
{txt}{p 0 4 2}
file {bf}
Data\CMP_average.dta{rm}
saved
{p_end}
{com}. 
. {c )-}
. {c )-}
{txt}
{com}. * Alternatively load prepared data
. {c -(}
. use "Data\CMP_average.dta", clear       
{txt}(Manifesto Project Dataset Version 2020a. Please type "notes" for more details)
{com}. {c )-}
{txt}
{com}. *##########################################
. * Analysis
. *##########################################
. // table A21: Partisan Polarization over Redistribution and Fixed Attributes
. {c -(}
. 
. preserve
.  
. eststo clear
. eststo: qui reg dist_av_welfare_policy L.dist_av_welfare_policy shock totseats  oecdmember  number2 i.year, cluster(countryname)
{txt}({res}est1{txt} stored)
{com}. eststo: qui reg dist_av_fixed L.dist_av_fixed shock totseats  oecdmember  number2 i.year, cluster(countryname)
{txt}({res}est2{txt} stored)
{com}. 
. esttab , replace label se title(Partisan Polarization over Redistribution and Fixed Attributes  \label {c -(}TabWithinpoldistav{c )-}) mti("Redistribution" "Fixed Values" "Redistribution" "Fixed Values" ) compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(shock  ) scalars( "N Observations" "r2 R$^2$" "aic AIC" ) indicate("Controls = tots*" "LDV = L.*" "FE Year = *year") 
{res}
{txt}Partisan Polarization over Redistribution and Fixed Attributes \label {TabWithinpoldistav}
{txt}{hline 42}
{txt}                       (1)          (2)   
{txt}                 Redistr~n    Fixed V~s   
{txt}{hline 42}
{txt}High LMP period {res}    -1.505        3.399***{txt}
                {res} {ralign 9:{txt:(}1.933{txt:)}}    {ralign 9:{txt:(}1.011{txt:)}}   {txt}
{txt}Controls        {res}       Yes          Yes   {txt}
{txt}LDV             {res}       Yes          Yes   {txt}
{txt}FE Year         {res}       Yes          Yes   {txt}
{txt}{hline 42}
{txt}Observations    {res}       186          186   {txt}
{txt}R$^2$           {res}     0.631        0.705   {txt}
{txt}AIC             {res}   899.206      889.795   {txt}
{txt}{hline 42}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}. 
. esttab using "Table\Tab_Within_d_av.tex", replace label se title(Partisan Polarization over Redistribution and Fixed Attributes  \label {c -(}TabWithinpoldistav{c )-}) mti("Redistribution" "Fixed Values" "Redistribution" "Fixed Values" ) compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(shock  ) scalars( "N Observations" "r2 R$^2$" "aic AIC" ) indicate("Controls = tots*" "LDV = L.*" "FE Year = *year") 
{res}{txt}(output written to {browse  `"Table\Tab_Within_d_av.tex"'})
{com}. 
. 
. 
. restore
. 
. {c )-}
{txt}
{com}. 
{txt}end of do-file

{com}. do "./Do/3_4_CMP_PRITM_Appendix_Dalton.do"
{txt}
{com}. *****************************************************************************
. * Cleaning and Analyzing - PRITM countries. Alternative proxy                           *
. *   . Polarization proxy as Dalton Index                                    *
. *                                               Manifesto Project Database                          *
. *                                                                                                                                           *
. * Author:                       Valentina Gonzalez-Rostani                                                      *
. * Contact:                      mag384@pitt.edu                                                                 *
. * Date:                         March 21 2021                                                                           *
. * Version:                      Stata 17                                                                        *
. *                                                                                                                                                       *
. *****************************************************************************
. 
. /*
> This do-file:
>         A. Call the Data
>         B. Define variables
>         C. Export Tables 
> 
> Input: ** Manifesto Project database**
>         - Data\CMP\MPDataset_MPDS2020a_stata14.dta // Data download from https://manifesto-project.wzb.eu/datasets 2021
> 
> 
> Final output:
>         Cleaned data: 
>                 * "Data\CMP_Dalton.dta" this data contains the relevant variables for the analysis with the DV as the polarization over redistribution, and fixed-value positions, estimated as Dalton index
>         Tables:
>                 * table A22: Partisan Polarization over Redistribution and Fixed Attributes, Dalton Index
> 
> 
> */
. cd "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication" // Only change your directory
{res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication
{txt}
{com}. 
. * Processing of the data (alternatively skip and go to line 175)
. {c -(}
. *##########################################
. * A. Calling the data
. *##########################################
. {c -(}
. 
. use "Data\CMP\MPDataset_MPDS2020a_stata14.dta", replace
{txt}(Manifesto Project Dataset Version 2020a. Please type "notes" for more details)
{com}. 
. {c )-}
. 
. *############################################
. * B. Creating Variables
. *############################################
. {c -(}
. * YEAR
. gen year = year(edate) 
. 
. * Keep countries of interest
. keep if countryname=="Australia" |  countryname=="Canada" |  countryname=="Greece" |  countryname=="New Zealand" |  countryname=="Portugal" |  countryname=="Spain" |  countryname=="United Kingdom" |  countryname=="United States" |  countryname=="France" |  countryname=="Norway" | countryname=="Austria" | countryname=="Belgium" | countryname=="Denmark" | countryname=="Estonia" | countryname=="Finland" | countryname=="Hungary" | countryname=="Germany" | countryname=="Iceland"  | countryname=="Ireland"  | countryname=="Italy"  | countryname=="Netherlands"  | countryname=="Norway" | countryname=="Slovakia" | countryname=="Slovenia" | countryname=="Sweden" | countryname=="Switzerland"
{txt}(1,682 observations deleted)
{com}. 
. * Defines a dummy to identify PRITM countries
. gen PRITM=. 
{txt}(2,900 missing values generated)
{com}. replace PRITM=0 if countryname=="Australia"
{txt}(111 real changes made)
{com}. replace PRITM=0 if countryname=="Canada"
{txt}(94 real changes made)
{com}. replace PRITM=0 if countryname=="Greece"
{txt}(84 real changes made)
{com}. replace PRITM=0 if countryname=="New Zealand"
{txt}(101 real changes made)
{com}. replace PRITM=0 if countryname=="Portugal"
{txt}(101 real changes made)
{com}. replace PRITM=0 if countryname=="Spain"
{txt}(157 real changes made)
{com}. replace PRITM=0 if countryname=="United Kingdom"
{txt}(98 real changes made)
{com}. replace PRITM=0 if countryname=="United States"
{txt}(53 real changes made)
{com}. replace PRITM=0 if countryname=="France"
{txt}(116 real changes made)
{com}. 
. replace PRITM=1 if countryname=="Austria"
{txt}(83 real changes made)
{com}. replace PRITM=1 if countryname=="Belgium"
{txt}(184 real changes made)
{com}. replace PRITM=1 if countryname=="Denmark"
{txt}(235 real changes made)
{com}. replace PRITM=1 if countryname=="Estonia"
{txt}(47 real changes made)
{com}. replace PRITM=1 if countryname=="Finland"
{txt}(162 real changes made)
{com}. replace PRITM=1 if countryname=="Germany"
{txt}(89 real changes made)
{com}. replace PRITM=1 if countryname=="Hungary"
{txt}(43 real changes made)
{com}. replace PRITM=1 if countryname=="Iceland"
{txt}(117 real changes made)
{com}. replace PRITM=1 if countryname=="Ireland"
{txt}(103 real changes made)
{com}. replace PRITM=1 if countryname=="Italy"
{txt}(178 real changes made)
{com}. replace PRITM=1 if countryname=="Netherlands"
{txt}(178 real changes made)
{com}. replace PRITM=1 if countryname=="Norway"
{txt}(130 real changes made)
{com}. replace PRITM=1 if countryname=="Slovenia"
{txt}(72 real changes made)
{com}. replace PRITM=1 if countryname=="Slovakia"
{txt}(69 real changes made)
{com}. replace PRITM=1 if countryname=="Sweden"
{txt}(137 real changes made)
{com}. replace PRITM=1 if countryname=="Switzerland"
{txt}(158 real changes made)
{com}. 
. 
. * CONTROL VARIABLES - number of parties
. 
. gen number = 1
. egen number2= sum(number), by(edate)
. lab var number2 "Number of parties"
. 
. *############################################################
. * DEPENDENT VARIABLE Redistribution and Fixed Attributes
. *############################################################
. {c -(}
. * Obtaining relevant policy variables
. 
. gen welfare_policy =  ln(per504+0.5) - ln(per505+0.5)
{txt}(7 missing values generated)
{com}. 
. gen fixed = .
{txt}(2,900 missing values generated)
{com}. replace fixed = (ln(per107+per108+per407+per602+0.5+per602_2)-ln(per109+per110+per406+per601+0.5+per601_2)) if !missing(per602_2) & !missing(per601_2)
{txt}(333 real changes made)
{com}. replace fixed = (ln(per107+per108+per407+per602+0.5)-ln(per109+per110+per406+per601+0.5)) if missing(per602_2) | missing(per601_2)
{txt}(2,560 real changes made)
{com}. 
. {c )-}
. 
. ** Different code compared to 3_2 do file: 
. ***** Now Dalton Index
. {c -(}
. egen pervote_e= sum(pervote), by(edate partyname)
. 
. foreach x of varlist welfare_policy fixed  {c -(}
{txt}  2{com}. 
. egen `x'_av= mean(`x'), by(edate countryname)
{txt}  3{com}. {c )-}       
. 
. foreach x of varlist welfare_policy fixed  {c -(}
{txt}  2{com}. 
. gen `x'_d= pervote_e*(`x'-`x'_av)^2
{txt}  3{com}. {c )-}
{txt}(7 missing values generated)
(7 missing values generated)
{com}. 
. foreach x of varlist welfare_policy fixed  {c -(}
{txt}  2{com}. 
. egen `x'_dt= sum(`x'_d), by(edate countryname)
{txt}  3{com}. gen `x'_dt2= (`x'_dt)^0.5
{txt}  4{com}. {c )-}       
. {c )-}
. 
. * Collapsing the data 
. {c -(}
. collapse (first) year  PRITM  totseats number2 oecdmember date *_dt2 *_dt , by(edate countryname)
{res}{com}. {c )-}
. // Final Prep of the data
. {c -(}
. egen country_number = group(countryname)
. 
. sort country_number edate
. bysort country_number: gen election_order=_n
. xtset country_number election_order
{res}
{col 1}{txt:Panel variable: }{res:country_number}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:election_order}{txt:, }{res:{bind:1}}{txt: to }{res:{bind:28}}{p_end}
{txt}{col 10}Delta: {res}1 unit
{com}. 
. 
. sort countryname  election_order
. 
. 
. 
. 
. sort countryname  year
. 
. gen shock=.
{txt}(474 missing values generated)
{com}. replace shock =0 if year > 1969 & year < 1995
{txt}(166 real changes made)
{com}. replace shock =1 if  year > 1994
{txt}(167 real changes made)
{com}. xtset country_number election_order
{res}
{col 1}{txt:Panel variable: }{res:country_number}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:election_order}{txt:, }{res:{bind:1}}{txt: to }{res:{bind:28}}{p_end}
{txt}{col 10}Delta: {res}1 unit
{com}. {c )-}
. {c )-}
. *############################################
. * Saving the data
. *############################################
. {c -(}
. lab var PRITM "PR with Trichotomous Multipartism"
. lab var totseats "Total Number of Seats"
. lab var number2 "Total Number of Parties"
. lab var oecdmember "OECD member"
. lab var welfare_policy_dt2 "Distance Redistribution (DR) - Net Welfare"
. lab var fixed_dt2 "Distance Fixed-Value Positions (DFVP) - Net Anti-Global"
. lab var PRITM "PRITM"
. lab var shock "High LMP period" // post 1994
. 
. keep  PRITM year  countryname  oecdmember totseats shock welfare_policy_dt2 fixed_dt2   number2 election_order country_number
. 
. keep if year>1969
{txt}(141 observations deleted)
{com}. keep if PRITM==1
{txt}(131 observations deleted)
{com}. 
. save "Data\CMP_Dalton.dta", replace
{txt}{p 0 4 2}
file {bf}
Data\CMP_Dalton.dta{rm}
saved
{p_end}
{com}. 
. {c )-}
. {c )-}
{txt}
{com}. * Alternatively load prepared data
. {c -(}
. use "Data\CMP_Dalton.dta", clear        
{txt}(Manifesto Project Dataset Version 2020a. Please type "notes" for more details)
{com}. {c )-}
{txt}
{com}. *##########################################
. * Analysis
. *##########################################
. {c -(}
. // table A22: Partisan Polarization over Redistribution and Fixed Attributes, Dalton Index
. {c -(}
. preserve
. keep if PRITM==1
{txt}(0 observations deleted)
{com}.    keep if year>1969 
{txt}(0 observations deleted)
{com}. 
. eststo clear
. // Redistribution
. eststo: qui reg welfare_policy_dt2 L.welfare_policy_dt2 shock totseats  oecdmember  number2 i.year, cluster(countryname)
{txt}({res}est1{txt} stored)
{com}. eststo: qui reg fixed_dt2 L.fixed_dt2 shock totseats  oecdmember  number2 i.year, cluster(countryname)
{txt}({res}est2{txt} stored)
{com}. 
. 
. 
. esttab , replace label se title(Polarization over Redistribution and Fixed Attributes between Mainstream Left and Right-Populist \label {c -(}TableCMPAllFE{c )-}) mti("Redistribution" "Fixed Values" "Redistribution" "Fixed Values" ) compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(shock ) scalars( "N Observations" "r2 R$^2$" "aic AIC" ) indicate("Controls = tots*" "LDV = L.*" "FE Year = *year") 
{res}
{txt}Polarization over Redistribution and Fixed Attributes between Mainstream Left and Right-Populist \label {TableCMPAllFE}
{txt}{hline 42}
{txt}                       (1)          (2)   
{txt}                 Redistr~n    Fixed V~s   
{txt}{hline 42}
{txt}High LMP period {res}   -12.476***     9.747***{txt}
                {res} {ralign 9:{txt:(}2.216{txt:)}}    {ralign 9:{txt:(}1.003{txt:)}}   {txt}
{txt}Controls        {res}       Yes          Yes   {txt}
{txt}LDV             {res}       Yes          Yes   {txt}
{txt}FE Year         {res}       Yes          Yes   {txt}
{txt}{hline 42}
{txt}Observations    {res}       186          186   {txt}
{txt}R$^2$           {res}     0.384        0.401   {txt}
{txt}AIC             {res}  1074.384     1031.899   {txt}
{txt}{hline 42}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}. 
. esttab using "Table\TabWithindalton.tex", replace label se title(Polarization over Redistribution and Fixed Attributes, Dalton Index \label {c -(}TabWithindalton{c )-}) mti("Redistribution" "Fixed Values" "Redistribution" "Fixed Values" ) compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(shock ) scalars( "N Observations" "r2 R$^2$" "aic AIC" ) indicate("Controls = tots*" "LDV = L.*" "FE Year = *year") 
{res}{txt}(output written to {browse  `"Table\TabWithindalton.tex"'})
{com}. 
. 
. 
. restore
. {c )-}
. {c )-}
{txt}
{com}. 
{txt}end of do-file

{com}. do "./Do/3_5_CMP_PRITM_Appendix_ER.do"
{txt}
{com}. *****************************************************************************
. * Cleaning and Analyzing - PRITM countries. Alternative proxy                           *
. *   . Polarization proxy as Esteban and Ray 1994        *
. *                                               Manifesto Project Database                          *
. *                                                                                                                                           *
. * Author:                       Valentina Gonzalez-Rostani                                                      *
. * Contact:                      mag384@pitt.edu                                                                 *
. * Date:                         March 21 2021                                                                           *
. * Version:                      Stata 17                                                                        *
. *                                                                                                                                                       *
. *****************************************************************************
. 
. /*
> This do-file:
>         A. Call the Data
>         B. Define variables
>         C. Export Tables 
> 
> Input: ** Manifesto Project database**
>         - Data\CMP\MPDataset_MPDS2020a_stata14.dta // Data download from https://manifesto-project.wzb.eu/datasets 2021
> 
> 
> Final output:
>         Cleaned data: 
>                 * "Data\CMP_ER.dta" this data contains the relevant variables for the analysis with the DV as the polarization over redistribution, and fixed-value positions, estimated following Esteban and Ray 1994.
>         Tables:
>                 * table A23: Partisan Polarization over Redistribution and Fixed Attributes
> 
> 
> */
. cd "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication" // Only change your directory
{res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication
{txt}
{com}. 
. * Processing of the data (alternatively skip and go to line 314)
. {c -(}
. *##########################################
. * A. Calling the data
. *##########################################
. {c -(}
. 
. use "Data\CMP\MPDataset_MPDS2020a_stata14.dta", replace
{txt}(Manifesto Project Dataset Version 2020a. Please type "notes" for more details)
{com}. 
. {c )-}
. *############################################
. * B. Creating Variables
. *############################################
. {c -(}
. * YEAR
. gen year = year(edate) 
. 
. * Keep countries of interest
. keep if countryname=="Australia" |  countryname=="Canada" |  countryname=="Greece" |  countryname=="New Zealand" |  countryname=="Portugal" |  countryname=="Spain" |  countryname=="United Kingdom" |  countryname=="United States" |  countryname=="France" |  countryname=="Norway" | countryname=="Austria" | countryname=="Belgium" | countryname=="Denmark" | countryname=="Estonia" | countryname=="Finland" | countryname=="Hungary" | countryname=="Germany" | countryname=="Iceland"  | countryname=="Ireland"  | countryname=="Italy"  | countryname=="Netherlands"  | countryname=="Norway" | countryname=="Slovakia" | countryname=="Slovenia" | countryname=="Sweden" | countryname=="Switzerland"
{txt}(1,682 observations deleted)
{com}. 
. * Defines a dummy to identify PRITM countries
. gen PRITM=. 
{txt}(2,900 missing values generated)
{com}. replace PRITM=0 if countryname=="Australia"
{txt}(111 real changes made)
{com}. replace PRITM=0 if countryname=="Canada"
{txt}(94 real changes made)
{com}. replace PRITM=0 if countryname=="Greece"
{txt}(84 real changes made)
{com}. replace PRITM=0 if countryname=="New Zealand"
{txt}(101 real changes made)
{com}. replace PRITM=0 if countryname=="Portugal"
{txt}(101 real changes made)
{com}. replace PRITM=0 if countryname=="Spain"
{txt}(157 real changes made)
{com}. replace PRITM=0 if countryname=="United Kingdom"
{txt}(98 real changes made)
{com}. replace PRITM=0 if countryname=="United States"
{txt}(53 real changes made)
{com}. replace PRITM=0 if countryname=="France"
{txt}(116 real changes made)
{com}. 
. replace PRITM=1 if countryname=="Austria"
{txt}(83 real changes made)
{com}. replace PRITM=1 if countryname=="Belgium"
{txt}(184 real changes made)
{com}. replace PRITM=1 if countryname=="Denmark"
{txt}(235 real changes made)
{com}. replace PRITM=1 if countryname=="Estonia"
{txt}(47 real changes made)
{com}. replace PRITM=1 if countryname=="Finland"
{txt}(162 real changes made)
{com}. replace PRITM=1 if countryname=="Germany"
{txt}(89 real changes made)
{com}. replace PRITM=1 if countryname=="Hungary"
{txt}(43 real changes made)
{com}. replace PRITM=1 if countryname=="Iceland"
{txt}(117 real changes made)
{com}. replace PRITM=1 if countryname=="Ireland"
{txt}(103 real changes made)
{com}. replace PRITM=1 if countryname=="Italy"
{txt}(178 real changes made)
{com}. replace PRITM=1 if countryname=="Netherlands"
{txt}(178 real changes made)
{com}. replace PRITM=1 if countryname=="Norway"
{txt}(130 real changes made)
{com}. replace PRITM=1 if countryname=="Slovenia"
{txt}(72 real changes made)
{com}. replace PRITM=1 if countryname=="Slovakia"
{txt}(69 real changes made)
{com}. replace PRITM=1 if countryname=="Sweden"
{txt}(137 real changes made)
{com}. replace PRITM=1 if countryname=="Switzerland"
{txt}(158 real changes made)
{com}. 
. 
. * CONTROL VARIABLES - number of parties
. 
. gen number = 1
. egen number2= sum(number), by(edate)
. lab var number2 "Number of parties"
. 
. *############################################################
. * DEPENDENT VARIABLE Redistribution and Fixed Attributes
. *############################################################
. * Obtaining relevant policy variables
. {c -(}
. gen welfare_policy =  ln(per504+0.5) - ln(per505+0.5)
{txt}(7 missing values generated)
{com}. 
. gen fixed = .
{txt}(2,900 missing values generated)
{com}. replace fixed = (ln(per107+per108+per407+per602+0.5+per602_2)-ln(per109+per110+per406+per601+0.5+per601_2)) if !missing(per602_2) & !missing(per601_2)
{txt}(333 real changes made)
{com}. replace fixed = (ln(per107+per108+per407+per602+0.5)-ln(per109+per110+per406+per601+0.5)) if missing(per602_2) | missing(per601_2)
{txt}(2,560 real changes made)
{com}. 
. {c )-}
. ** Different code compared to 3_2 do file: 
. {c -(}
. **  Mainstream left: - soc social democratic and socialist or other left
. gen m_left = . 
{txt}(2,900 missing values generated)
{com}. replace m_left = 1 if parfam== 30 | parfam== 20
{txt}(904 real changes made)
{com}. replace m_left = 0 if parfam ~= 30 & parfam ~=20 & parfam ~=. 
{txt}(1,996 real changes made)
{com}. 
. egen m_left_participation= max(m_left), by(edate)
. 
. 
. **  Mainstream left: only soc social democratic
. gen m_left_restrict = . 
{txt}(2,900 missing values generated)
{com}. replace m_left_restrict = 1 if parfam== 30
{txt}(577 real changes made)
{com}. replace m_left_restrict = 0 if parfam ~= 30 & parfam ~=. 
{txt}(2,323 real changes made)
{com}. 
. ** Other left: Ecologista and socialist
. gen o_left = . 
{txt}(2,900 missing values generated)
{com}. replace o_left = 1 if  parfam== 10  // here I am also including socialist or other left
{txt}(147 real changes made)
{com}. replace o_left = 0 if parfam ~= 10  & parfam ~=. 
{txt}(2,753 real changes made)
{com}. 
. egen o_left_participation= max(o_left), by(edate)
. 
. ** Mainstream right: lib liberal, Christian Democrat, Conservatives
. gen m_right = . 
{txt}(2,900 missing values generated)
{com}. replace m_right = 1 if parfam == 40 | parfam == 50 | parfam == 60
{txt}(1,174 real changes made)
{com}. replace m_right = 0 if parfam ~= 40 & parfam ~= 50 & parfam ~= 60 & parfam ~=. 
{txt}(1,726 real changes made)
{com}. 
. egen m_right_participation= max(m_right), by(edate)
. 
. ** Other right: agriculture
. gen o_right = . 
{txt}(2,900 missing values generated)
{com}. replace o_right = 1 if parfam == 80
{txt}(133 real changes made)
{com}. replace o_right = 0 if parfam ~= 80 & parfam ~=. 
{txt}(2,767 real changes made)
{com}. 
. egen o_right_participation= max(o_right), by(edate)
. 
. ** Radical right:  nat nationalist 
. gen rad_right = . 
{txt}(2,900 missing values generated)
{com}. replace rad_right = 1 if parfam == 70 
{txt}(221 real changes made)
{com}. replace rad_right = 0 if parfam ~= 70 & parfam ~=. 
{txt}(2,679 real changes made)
{com}. 
. egen rad_right_participation= max(rad_right), by(edate)
. 
. 
. ** Other parties: special issues and Ethnic and regional parties
. gen o_parties = . 
{txt}(2,900 missing values generated)
{com}. replace o_parties = 1 if parfam == 90 | parfam == 95 
{txt}(321 real changes made)
{com}. replace o_parties = 0 if parfam ~= 90 & parfam ~= 95  & parfam ~=. 
{txt}(2,579 real changes made)
{com}. 
. egen o_parties_participation= max(o_parties), by(edate)
. 
. 
. ** DISTANCE
. {c -(}
. foreach x of varlist welfare_policy  fixed  {c -(}
{txt}  2{com}. 
. gen `x'_m_left = m_left*`x' if m_left==1
{txt}  3{com}. gen `x'_rad_right = rad_right*`x' if rad_right==1
{txt}  4{com}. gen `x'_m_right = m_right*`x' if m_right==1
{txt}  5{com}. 
. gen `x'_o_left = o_left*`x' if o_left==1
{txt}  6{com}. gen `x'_o_right = o_right*`x' if o_right==1
{txt}  7{com}. gen `x'_o_parties = o_parties*`x' if o_parties==1
{txt}  8{com}. 
. replace `x'_m_left = 0 if `x'_m_left ==.
{txt}  9{com}. replace `x'_rad_right = 0 if `x'_rad_right ==.
{txt} 10{com}. replace `x'_m_right = 0 if `x'_m_right ==.
{txt} 11{com}. replace `x'_o_left = 0 if `x'_o_left ==.
{txt} 12{com}. replace `x'_o_right = 0 if `x'_o_right ==.
{txt} 13{com}. replace `x'_o_parties = 0 if `x'_o_parties ==.
{txt} 14{com}.         
. {c )-}
{txt}(1,998 missing values generated)
(2,682 missing values generated)
(1,727 missing values generated)
(2,753 missing values generated)
(2,767 missing values generated)
(2,580 missing values generated)
(1,998 real changes made)
(2,682 real changes made)
(1,727 real changes made)
(2,753 real changes made)
(2,767 real changes made)
(2,580 real changes made)
(1,998 missing values generated)
(2,682 missing values generated)
(1,727 missing values generated)
(2,753 missing values generated)
(2,767 missing values generated)
(2,580 missing values generated)
(1,998 real changes made)
(2,682 real changes made)
(1,727 real changes made)
(2,753 real changes made)
(2,767 real changes made)
(2,580 real changes made)
{com}. 
. foreach x of varlist m_left rad_right m_right o_left o_right o_parties {c -(}
{txt}  2{com}. 
. egen vote_`x' = mean(pervote), by(countryname edate `x')
{txt}  3{com}. replace vote_`x'=. if `x'==0
{txt}  4{com}. {c )-}
{txt}(4 missing values generated)
(1,996 real changes made, 1,996 to missing)
(2,679 real changes made, 2,679 to missing)
(1,726 real changes made, 1,726 to missing)
(1 missing value generated)
(2,753 real changes made, 2,753 to missing)
(1 missing value generated)
(2,767 real changes made, 2,767 to missing)
(2,579 real changes made, 2,579 to missing)
{com}. 
. 
. 
. sort countryname edate
. {c )-}
. * Collapsing the data 
. {c -(}
. collapse (sum) fixed* welf*  (first) *_participation year  PRITM  totseats number2 oecdmember date (mean) vote_* , by(edate countryname)
{res}{com}. {c )-}
. // Final Prep of the data
. {c -(}
. egen country_number = group(countryname)
. 
. sort country_number edate
. bysort country_number: gen election_order=_n
. xtset country_number election_order
{res}
{col 1}{txt:Panel variable: }{res:country_number}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:election_order}{txt:, }{res:{bind:1}}{txt: to }{res:{bind:28}}{p_end}
{txt}{col 10}Delta: {res}1 unit
{com}. 
. 
. gen alpha=1.6
. 
. 
. 
. 
. foreach x of varlist  welfare_policy fixed {c -(}
{txt}  2{com}. 
. * Distance
. // Mainstream left - other left
. gen `x'_ml_ol =  vote_m_left^(alpha)*vote_o_left^(alpha)*abs(`x'_m_left -`x'_o_left)
{txt}  3{com}. replace `x'_ml_ol =  0 if o_left_participation==0
{txt}  4{com}. 
. // Mainstream left - mainstream right
. gen `x'_ml_mr =  vote_m_left^(alpha)*vote_m_right^(alpha)*abs(`x'_m_left -`x'_m_right)
{txt}  5{com}. replace `x'_ml_mr =  0 if m_right_participation==0
{txt}  6{com}. 
. // Mainstream left - radical right
. gen `x'_ml_rr =  vote_m_left^(alpha)*vote_rad_right^(alpha)*abs(`x'_m_left -`x'_rad_right)
{txt}  7{com}. replace `x'_ml_rr =  0 if rad_right_participation==0
{txt}  8{com}. 
. // Mainstream left - other right
. gen `x'_ml_or =  vote_m_left^(alpha)*vote_o_right^(alpha)*abs(`x'_m_left -`x'_o_right)
{txt}  9{com}. replace `x'_ml_or =  0 if o_right_participation==0
{txt} 10{com}. 
. // Mainstream left - other parties
. gen `x'_ml_op =  vote_m_left^(alpha)*vote_o_parties^(alpha)*abs(`x'_m_left -`x'_o_parties)
{txt} 11{com}. replace `x'_ml_op =  0 if o_parties_participation==0
{txt} 12{com}. 
. //////
> // Mainstream right - other left
. gen `x'_mr_ol =  vote_m_right^(alpha)*vote_o_left^(alpha)*abs(`x'_m_right -`x'_o_left)
{txt} 13{com}. replace `x'_mr_ol =  0 if o_left_participation==0
{txt} 14{com}. 
. // Mainstream right - radical right
. gen `x'_mr_rr =  vote_m_right^(alpha)*vote_rad_right^(alpha)*abs(`x'_m_right -`x'_rad_right)
{txt} 15{com}. replace `x'_mr_rr =  0 if rad_right_participation==0
{txt} 16{com}. 
. // Mainstream right - other right
. gen `x'_mr_or =  vote_m_right^(alpha)*vote_o_right^(alpha)*abs(`x'_m_right -`x'_o_right)
{txt} 17{com}. replace `x'_mr_or =  0 if o_right_participation==0
{txt} 18{com}. 
. // Mainstream right - other party
. gen `x'_mr_op =  vote_m_right^(alpha)*vote_o_parties^(alpha)*abs(`x'_m_right -`x'_o_parties)
{txt} 19{com}. replace `x'_mr_op =  0 if `x'_mr_op==.
{txt} 20{com}. 
. 
. ///// Radical right
> // Radical right - other left
. gen `x'_rr_ol =  vote_rad_right^(alpha)*vote_o_left^(alpha)*abs(`x'_rad_right -`x'_o_left)
{txt} 21{com}. replace `x'_rr_ol =  0 if o_left_participation==0
{txt} 22{com}. 
. // Radical right - other right
. gen `x'_rr_or =  vote_rad_right^(alpha)*vote_o_right^(alpha)*abs(`x'_rad_right -`x'_o_right)
{txt} 23{com}. replace `x'_rr_or =  0 if o_right_participation==0
{txt} 24{com}. 
. // Radical right - other party
. gen `x'_rr_op =  vote_rad_right^(alpha)*vote_o_parties^(alpha)*abs(`x'_rad_right -`x'_o_parties)
{txt} 25{com}. replace `x'_rr_op =  0 if o_parties_participation==0
{txt} 26{com}. 
. ///// Other left
> // Other left - other right
. gen `x'_ol_or =  vote_o_left^(alpha)*vote_o_parties^(alpha)*abs(`x'_o_left -`x'_o_right)
{txt} 27{com}. replace `x'_ol_or =  0 if o_right_participation==0
{txt} 28{com}. 
. // Other left - other party
. gen `x'_ol_op =  vote_o_left^(alpha)*vote_o_parties^(alpha)*abs(`x'_o_left -`x'_o_parties)
{txt} 29{com}. replace `x'_ol_op =  0 if o_parties_participation==0
{txt} 30{com}. 
. //// Other right
> // Other right- other party
. gen `x'_or_op =  vote_o_right^(alpha)*vote_o_parties^(alpha)*abs(`x'_o_right -`x'_o_parties)
{txt} 31{com}. replace `x'_or_op =  0 if o_parties_participation==0
{txt} 32{com}. 
. gen distance_`x' = `x'_ml_ol+ `x'_ml_mr+ `x'_ml_rr+ `x'_ml_or+ `x'_ml_op+ `x'_mr_ol+ `x'_mr_rr+ `x'_mr_or +`x'_mr_op+ `x'_rr_ol+ `x'_rr_or+ `x'_rr_op+ `x'_ol_or+ `x'_ol_op+ `x'_or_op
{txt} 33{com}. 
. gen distance_`x'2 = `x'_ml_mr+ `x'_ml_rr+ `x'_ml_or+ `x'_mr_rr+ `x'_mr_or  + `x'_rr_or
{txt} 34{com}. gen distance_`x'3 = `x'_ml_mr+ `x'_ml_rr+ `x'_mr_rr  
{txt} 35{com}. 
. 
. 
. 
. 
. {c )-}
{txt}(344 missing values generated)
(342 real changes made)
(1 missing value generated)
(0 real changes made)
(292 missing values generated)
(287 real changes made)
(345 missing values generated)
(338 real changes made)
(294 missing values generated)
(289 real changes made)
(344 missing values generated)
(342 real changes made)
(291 missing values generated)
(287 real changes made)
(345 missing values generated)
(338 real changes made)
(293 missing values generated)
(293 real changes made)
(394 missing values generated)
(342 real changes made)
(435 missing values generated)
(338 real changes made)
(396 missing values generated)
(289 real changes made)
(410 missing values generated)
(337 real changes made)
(410 missing values generated)
(289 real changes made)
(440 missing values generated)
(289 real changes made)
(289 missing values generated)
(100 missing values generated)
(5 missing values generated)
(344 missing values generated)
(342 real changes made)
(1 missing value generated)
(0 real changes made)
(292 missing values generated)
(287 real changes made)
(345 missing values generated)
(338 real changes made)
(294 missing values generated)
(289 real changes made)
(344 missing values generated)
(342 real changes made)
(291 missing values generated)
(287 real changes made)
(345 missing values generated)
(338 real changes made)
(293 missing values generated)
(293 real changes made)
(394 missing values generated)
(342 real changes made)
(435 missing values generated)
(338 real changes made)
(396 missing values generated)
(289 real changes made)
(410 missing values generated)
(337 real changes made)
(410 missing values generated)
(289 real changes made)
(440 missing values generated)
(289 real changes made)
(289 missing values generated)
(100 missing values generated)
(5 missing values generated)
{com}. 
. gen shock=.
{txt}(474 missing values generated)
{com}. replace shock =0 if year > 1969 & year < 1995
{txt}(166 real changes made)
{com}. replace shock =1 if  year > 1994        
{txt}(167 real changes made)
{com}. {c )-}
. {c )-}
. {c )-}
. *############################################
. * Saving the data
. *############################################
. {c -(}
. 
. lab var PRITM "PR with Trichotomous Multipartism"
. lab var totseats "Total Number of Seats"
. lab var number2 "Total Number of Parties"
. lab var oecdmember "OECD member"
. lab var PRITM "PRITM"
. 
. lab var distance_welfare_policy3 "Distance Redistribution (DR) - Net Welfare"
. lab var distance_fixed3 "Distance Fixed-Value Positions (DFVP) - Net Anti-Global"
. 
. lab var shock "High LMP period" // post 1994
. 
. keep  PRITM year  countryname  oecdmember totseats shock distance_welfare_policy3 distance_fixed3   number2 election_order country_number
. 
. keep if year>1969
{txt}(141 observations deleted)
{com}. keep if PRITM==1
{txt}(131 observations deleted)
{com}. 
. save "Data\CMP_ER.dta", replace
{txt}{p 0 4 2}
file {bf}
Data\CMP_ER.dta{rm}
saved
{p_end}
{com}. 
. {c )-}
. {c )-}
{txt}
{com}. * Alternatively load prepared data
. {c -(}
. use "Data\CMP_ER.dta", clear    
{txt}(Manifesto Project Dataset Version 2020a. Please type "notes" for more details)
{com}. {c )-}
{txt}
{com}. *##########################################
. * Analysis
. *##########################################
. {c -(}
. 
. // table A23: Partisan Polarization over Redistribution and Fixed Attributes
. {c -(}
. preserve
. 
. eststo clear
. eststo: qui reg distance_welfare_policy3 L.distance_welfare_policy3 shock totseats  oecdmember  number2 , cluster(countryname)
{txt}({res}est1{txt} stored)
{com}. eststo: qui reg distance_fixed3 L.distance_fixed3 shock totseats  oecdmember  number2 , cluster(countryname)
{txt}({res}est2{txt} stored)
{com}. 
. 
. 
. esttab , replace label se title(Partisan Polarization over Redistribution and Fixed Attributes  \label {c -(}TabWithinER{c )-}) mti("Redistribution" "Fixed Values" "Redistribution" "Fixed Values") compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(shock    ) scalars( "N Observations" "r2 R$^2$" "aic AIC" ) indicate("Control variables = tot*" "LDV = L.*" )
{res}
{txt}Partisan Polarization over Redistribution and Fixed Attributes \label {TabWithinER}
{txt}{hline 42}
{txt}                       (1)          (2)   
{txt}                 Redistr~n    Fixed V~s   
{txt}{hline 42}
{txt}High LMP period {res}  -3.3e+03      1.1e+04***{txt}
                {res} {ralign 9:{txt:(}1.3e+04{txt:)}}    {ralign 9:{txt:(}3248.358{txt:)}}   {txt}
{txt}Control variab~s{res}       Yes          Yes   {txt}
{txt}LDV             {res}       Yes          Yes   {txt}
{txt}{hline 42}
{txt}Observations    {res}       180          180   {txt}
{txt}R$^2$           {res}     0.326        0.295   {txt}
{txt}AIC             {res}  4589.552     4358.513   {txt}
{txt}{hline 42}
{txt}Standard errors in parentheses
{txt}* p<0.1, ** p<0.05, *** p<0.01
{com}. 
. esttab using "Table\TabWithin_ER.tex", replace label se title(Partisan Polarization over Redistribution and Fixed Attributes  \label {c -(}TabWithinER{c )-}) mti("Redistribution" "Fixed Values" "Redistribution" "Fixed Values") compress nogap star(* 0.1 ** 0.05 *** 0.01) b(%6.3f) keep(shock    ) scalars( "N Observations" "r2 R$^2$" "aic AIC" ) indicate("Control variables = tot*" "LDV = L.*" )
{res}{txt}(output written to {browse  `"Table\TabWithin_ER.tex"'})
{com}. 
. restore
. {c )-}
. {c )-}
{txt}
{com}. 
{txt}end of do-file

{com}. do "./Do/4_1_Figures_ISSP.do"
{txt}
{com}. *****************************************************************************
. *                                 Figures Descriptives with ISSP                                        *
. *                                                                                                                                                       *                       
. * Author:                       Valentina Gonzalez Rostani                                                      *
. * Contact:                      mag384@pitt.edu                                                                 *
. * Date:                         August 9 2024                                                                                   *
. * Version:                      Stata 17                                                                                                *                                                                          
. *                                                                                                                                                       *
. *****************************************************************************
. /*
> This do-file:
> - Creates Figure 1, 2 and A3 using data from ISSP. 
> 
> Input:
> - Data\Figures_ISSP.dta
> 
> Output:
> - Figure 1: Relative Share of Labor Force 1995 to 2014 [Figures\Relative Share of Labor Force 1998 to 2014 ISSP.pdf]
> - Figure 2: Electoral consequences, Routine and Non-Routine Voters [Figure/price_by_mpg.pdf]
> - Figure A3: Importance of job security, Difficulties to find a new job, Concerns about losing the job and Job dissatisfaction [Figure/jobdisatisfactionpredictedtogetherall.pdf]
> */
. 
. *Defining Directory
. cd "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication"
{res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication
{txt}
{com}. 
. *Calling the data
. use "Data\Figures_ISSP.dta", clear 
{txt}
{com}. 
. *******************************************************************************
. * Graphs
. *******************************************************************************
. * Graph style for F1 & 2
. {c -(}
. grstyle clear
. set scheme s2color
. grstyle init
{res}{com}. grstyle set plain, box
. grstyle color background white
. grstyle color major_grid gs8
. grstyle linepattern major_grid dot
. {c )-}
{txt}
{com}. // Figure 1: Relative Share of Labor Force 1995 to 2014
. {c -(}
.     * Generating summary statistics with three task categories:
.     * Task 1, Task 2, and Task 33 following Autor (2003) and coded by Kurer and Gallego (2019)
.     
.     * Preserve the current dataset in memory so it can be restored later
.     preserve 
.     
.     * Collapse the data to calculate the mean of task1, task2, and task33 weighted by 'weight' for each year
.     collapse task1 task2 task33 [aw=weight], by(year)
{res}{com}.     
.     * Keep only the observations where the year is greater than 1997 to look post automation shock
.     keep if year > 1997
{txt}(3 observations deleted)
{com}.     
.     * Create a line graph for the task categories over time
.     * The first line represents Non-Routine Cognitive tasks (green line)
.  graph twoway line  task1 year if year>1997, lc(green) legend(label(1 "Non-Routine Cognitive")) || ///
>   line  task2 year if year>1997, lc(red) legend(label(2 "Routine")) || ///
>     line  task33 year if year>1997, lc(blue) legend(label(3 "Non-Routine Manual")) 
{res}{com}. 
.     
.     * Export the graph as a PDF file with the specified name, replacing any existing file
.     graph export "Figure\Relative Share of Labor Force 1998 to 2014 ISSP.pdf", as(pdf) replace
{txt}{p 0 4 2}
file {bf}
Figure\Relative Share of Labor Force 1998 to 2014 ISSP.pdf{rm}
saved as
PDF
format
{p_end}
{com}.     
.     * Restore the original dataset that was in memory before the collapse
.     restore
. {c )-}
{txt}
{com}. 
. // Figure 2: Electoral consequences, Routine and Non-Routine Voters
. {c -(}
. * Here I look at the share of votes by party family, and instead of using the three categories of risks, I put together non-routine manual and routine. I repeat this for RR, ML, MR, Non-voters. Code commented in the first one. 
. // Radical right (graph commented)
. {c -(}
. * Label the variable 'radicalR' to represent "Votes for Right Populist (%)"
. lab var radicalR "Votes for Right Populist (%)"
. 
. * Preserve the current dataset in memory so it can be restored later
. preserve        
. 
. * Generate a new variable 'task2and3' initialized to 1 if 'task2' equals 1
. gen task2and3 = 1 if task2 == 1
{txt}(162,633 missing values generated)
{com}. 
. * Replace 'task2and3' with 1 if 'task33' equals 1 (combining task2 and task33)
. replace task2and3 = 1 if task33 == 1
{txt}(43,947 real changes made)
{com}. 
. * Replace 'task2and3' with 0 if 'task1' equals 1 (Non-Routine Cognitive tasks)
. replace task2and3 = 0 if task1 == 1
{txt}(71,529 real changes made)
{com}. 
. * Keep only observations where 'emplB' is less than 6 (likely filtering by employment status or category)
. keep if emplB < 6
{txt}(17,803 observations deleted)
{com}. 
. * Collapse the data to calculate the mean of 'radicalR' weighted by 'weight' for each year, task1, and task2and3 categories
. collapse radicalR [aw=weight], by(year task1 task2and3)
{res}{com}. 
. * Create a line graph for 'radicalR' over time, segmented by task categories
. graph twoway line  radicalR year if task1==1, lc(green) legend(label(1 "Non-Routine Cognitive"))  || ///
>   line  radicalR year if task2and3==1, lc(red) legend(label(2 "Routine & Manual")) ytitle("Votes for Right Populist (%)", size(small))    legend(size(vsmall))  xtitle("Year", size(small))   ylabel(, labsize(vsmall))  xlabel(, labsize(vsmall))
{res}{com}. 
. * Save the graph as "RR.gph" in the "Figure" directory, replacing any existing file with the same name
. graph save "Figure/RR.gph", replace
{txt}{p 0 4 2}
(file {bf}
Figure/RR.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Figure/RR.gph} saved
{com}. 
. * Restore the original dataset that was in memory before the modifications
. restore
. {c )-}
. // Maintream left
. {c -(}
.  preserve       
. 
. gen task2and3=1 if task2==1
{txt}(162,633 missing values generated)
{com}. replace task2and3=1 if task33==1
{txt}(43,947 real changes made)
{com}. replace task2and3=0 if task1==1
{txt}(71,529 real changes made)
{com}. 
. keep if emplB<6
{txt}(17,803 observations deleted)
{com}. collapse mainstreamleft [aw=weight], by(year task1 task2and3)
{res}{com}. 
. graph twoway line  mainstreamleft year if task1==1, lc(green)   legend(label(1 "Non-Routine Cognitive"))  || ///
>   line  mainstreamleft year if task2and3==1, lc(red) legend(label(2 "Routine & Manual"))  ytitle("Votes for Mainstream Left (%)", size(small))    legend(size(vsmall))  xtitle("Year", size(small))   ylabel(, labsize(vsmall))  xlabel(, labsize(vsmall))
{res}{com}. graph save "Figure/ML.gph", replace
{txt}{p 0 4 2}
(file {bf}
Figure/ML.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Figure/ML.gph} saved
{com}. 
. restore 
. {c )-}
. // Mainstream right
. {c -(}
.  preserve       
. 
. gen task2and3=1 if task2==1
{txt}(162,633 missing values generated)
{com}. replace task2and3=1 if task33==1
{txt}(43,947 real changes made)
{com}. replace task2and3=0 if task1==1
{txt}(71,529 real changes made)
{com}. 
. keep if emplB<6
{txt}(17,803 observations deleted)
{com}. collapse mainstreamright [aw=weight], by(year task1 task2and3)
{res}{com}. 
. graph twoway line  mainstreamright year if task1==1, lc(green)  legend(label(1 "Non-Routine Cognitive"))  || ///
>   line  mainstreamright year if task2and3==1, lc(red) legend(label(2 "Routine & Manual"))  ytitle("Votes for Mainstream Right (%)", size(small))    legend(size(vsmall))  xtitle("Year", size(small))   ylabel(, labsize(vsmall))  xlabel(, labsize(vsmall))
{res}{com}. graph save "Figure/MR.gph", replace
{txt}{p 0 4 2}
(file {bf}
Figure/MR.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Figure/MR.gph} saved
{com}. 
. restore 
. {c )-}
. // Non Voters
. {c -(}
.  preserve       
. 
. gen task2and3=1 if task2==1
{txt}(162,633 missing values generated)
{com}. replace task2and3=1 if task33==1
{txt}(43,947 real changes made)
{com}. replace task2and3=0 if task1==1
{txt}(71,529 real changes made)
{com}. 
. keep if emplB<6
{txt}(17,803 observations deleted)
{com}. collapse nonvoters [aw=weight], by(year task1 task2and3)
{res}{com}. 
. graph twoway line  nonvoters year if task1==1, lc(green)  legend(label(1 "Non-Routine Cognitive"))  || ///
>   line  nonvoters year if task2and3==1, lc(red) legend(label(2 "Routine & Manual"))   ytitle("Non-Voters (%)", size(small))    legend(size(vsmall))  xtitle("Year", size(small))   ylabel(, labsize(vsmall))  xlabel(, labsize(vsmall))
{res}{com}. graph save "Figure/Nv.gph", replace
{txt}{p 0 4 2}
(file {bf}
Figure/Nv.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Figure/Nv.gph} saved
{com}. 
. restore 
. {c )-}
.  * Combine multiple graphs into a single figure
. graph combine "Figure/ML.gph" "Figure/MR.gph" "Figure/RR.gph" "Figure/Nv.gph"
{res}{com}. * Export the combined graph as a PDF file named "price_by_mpg.pdf" in the "Figure" directory
. graph export "Figure/price_by_mpg.pdf", as(pdf) replace
{txt}{p 0 4 2}
file {bf}
Figure/price_by_mpg.pdf{rm}
saved as
PDF
format
{p_end}
{com}. 
. * Erase (delete) the individual graph files from the "Figure" directory after combining them
. erase "Figure/ML.gph"
. erase "Figure/MR.gph"
. erase "Figure/RR.gph"
. erase "Figure/Nv.gph"
. 
. 
. 
. {c )-}
{txt}
{com}. // Figure A3: Importance of job security, Difficulties to find a new job, Concerns about losing the job and Job dissatisfaction
. {c -(}
. * The code categorizes individuals based on whether they find it difficult to get a new job, then runs a logistic regression to assess how the risk of automation influences this difficulty, adjusting for weights. It calculates the predicted probabilities of job difficulty across different levels of automation risk and generates a graph to visualize these probabilities, saving the result as a file.
. * Code commented for first graph whether it is difficult to find a new job. Then code for the other three is similar (levels of variables changes so the dummies are different). 
. 
. 
. * Graph style for figure A3
. {c -(}
. grstyle clear
. set scheme s2color
. grstyle init
{res}{com}. grstyle set plain, nogrid
. grstyle color background white
. {c )-}
. * W_easynewjob
. {c -(}
. * Generate a new variable 'W_easynewjob_difficult' to categorize the difficulty of finding a new job
. * Set 'W_easynewjob_difficult' to 1 if 'W_easynewjob' is 4 or 5 (indicating it is difficult)
. gen W_easynewjob_difficult = 1 if W_easynewjob == 4 | W_easynewjob == 5
{txt}(189,640 missing values generated)
{com}. * Replace 'W_easynewjob_difficult' with 0 if 'W_easynewjob' is 1, 2, or 3 (indicating it is not difficult)
. replace W_easynewjob_difficult = 0 if W_easynewjob == 1 | W_easynewjob == 2 | W_easynewjob == 3
{txt}(12,317 real changes made)
{com}. 
. * Run a logistic regression model with 'W_easynewjob_difficult' as the dependent variable
. logit W_easynewjob_difficult rti [pweight=weight]

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -14737.17}  
Iteration 1:{space 3}log pseudolikelihood = {res:-14666.246}  
Iteration 2:{space 3}log pseudolikelihood = {res:-14666.231}  
Iteration 3:{space 3}log pseudolikelihood = {res:-14666.231}  
{res}
{txt}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:21,420}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:126.26}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}Log pseudolikelihood = {res:-14666.231}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0048}

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}W_easynewjob_difficult{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      z{col 56}   P>|z|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}rti {c |}{col 24}{res}{space 2} .1619526{col 36}{space 2} .0144131{col 47}{space 1}   11.24{col 56}{space 3}0.000{col 64}{space 4} .1337034{col 77}{space 3} .1902019
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .1418846{col 36}{space 2} .0145724{col 47}{space 1}    9.74{col 56}{space 3}0.000{col 64}{space 4} .1133232{col 77}{space 3} .1704461
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. 
. * Calculate the marginal effects of 'rti' on the predicted probability of 'W_easynewjob_difficult'
. * atmeans calculates the marginal effect at the means of the covariates
. * at() specifies a range of values for 'rti' from -1.52 to 2.24 with increments of 0.05
. margins, atmeans at(rti=(-1.52(0.05)2.24))
{res}
{txt}Adjusted predictions{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:21,420}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(W_easynewjob_difficult), predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.52}}
{lalign 8:2._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.47}}
{lalign 8:3._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.42}}
{lalign 8:4._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.37}}
{lalign 8:5._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.32}}
{lalign 8:6._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.27}}
{lalign 8:7._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.22}}
{lalign 8:8._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.17}}
{lalign 8:9._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.12}}
{lalign 8:10._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.07}}
{lalign 8:11._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.02}}
{lalign 8:12._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.97}}
{lalign 8:13._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.92}}
{lalign 8:14._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.87}}
{lalign 8:15._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.82}}
{lalign 8:16._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.77}}
{lalign 8:17._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.72}}
{lalign 8:18._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.67}}
{lalign 8:19._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.62}}
{lalign 8:20._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.57}}
{lalign 8:21._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.52}}
{lalign 8:22._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.47}}
{lalign 8:23._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.42}}
{lalign 8:24._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.37}}
{lalign 8:25._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.32}}
{lalign 8:26._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.27}}
{lalign 8:27._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.22}}
{lalign 8:28._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.17}}
{lalign 8:29._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.12}}
{lalign 8:30._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.07}}
{lalign 8:31._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.02}}
{lalign 8:32._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.03}}
{lalign 8:33._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.08}}
{lalign 8:34._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.13}}
{lalign 8:35._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.18}}
{lalign 8:36._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.23}}
{lalign 8:37._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.28}}
{lalign 8:38._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.33}}
{lalign 8:39._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.38}}
{lalign 8:40._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.43}}
{lalign 8:41._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.48}}
{lalign 8:42._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.53}}
{lalign 8:43._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.58}}
{lalign 8:44._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.63}}
{lalign 8:45._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.68}}
{lalign 8:46._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.73}}
{lalign 8:47._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.78}}
{lalign 8:48._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.83}}
{lalign 8:49._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.88}}
{lalign 8:50._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.93}}
{lalign 8:51._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.98}}
{lalign 8:52._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.03}}
{lalign 8:53._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.08}}
{lalign 8:54._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.13}}
{lalign 8:55._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.18}}
{lalign 8:56._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.23}}
{lalign 8:57._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.28}}
{lalign 8:58._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.33}}
{lalign 8:59._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.38}}
{lalign 8:60._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.43}}
{lalign 8:61._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.48}}
{lalign 8:62._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.53}}
{lalign 8:63._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.58}}
{lalign 8:64._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.63}}
{lalign 8:65._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.68}}
{lalign 8:66._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.73}}
{lalign 8:67._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.78}}
{lalign 8:68._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.83}}
{lalign 8:69._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.88}}
{lalign 8:70._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.93}}
{lalign 8:71._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.98}}
{lalign 8:72._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.03}}
{lalign 8:73._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.08}}
{lalign 8:74._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.13}}
{lalign 8:75._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.18}}
{lalign 8:76._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.23}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .4739528{col 26}{space 2} .0062495{col 37}{space 1}   75.84{col 46}{space 3}0.000{col 54}{space 4}  .461704{col 67}{space 3} .4862016
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .4759721{col 26}{space 2} .0061063{col 37}{space 1}   77.95{col 46}{space 3}0.000{col 54}{space 4}  .464004{col 67}{space 3} .4879402
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .4779922{col 26}{space 2} .0059646{col 37}{space 1}   80.14{col 46}{space 3}0.000{col 54}{space 4} .4663018{col 67}{space 3} .4896827
{txt}{space 10}4  {c |}{col 14}{res}{space 2}  .480013{col 26}{space 2} .0058247{col 37}{space 1}   82.41{col 46}{space 3}0.000{col 54}{space 4} .4685969{col 67}{space 3} .4914292
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .4820345{col 26}{space 2} .0056866{col 37}{space 1}   84.77{col 46}{space 3}0.000{col 54}{space 4} .4708891{col 67}{space 3}   .49318
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{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. 
. * Plot the margins with a line graph and display the confidence intervals as dotted lines
.  margins, atmeans at(rti=(-1.52(0.05)2.24)) 
{res}
{txt}Adjusted predictions{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:21,420}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(W_easynewjob_difficult), predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.52}}
{lalign 8:2._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.47}}
{lalign 8:3._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.42}}
{lalign 8:4._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.37}}
{lalign 8:5._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.32}}
{lalign 8:6._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.27}}
{lalign 8:7._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.22}}
{lalign 8:8._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.17}}
{lalign 8:9._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.12}}
{lalign 8:10._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.07}}
{lalign 8:11._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.02}}
{lalign 8:12._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.97}}
{lalign 8:13._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.92}}
{lalign 8:14._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.87}}
{lalign 8:15._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.82}}
{lalign 8:16._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.77}}
{lalign 8:17._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.72}}
{lalign 8:18._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.67}}
{lalign 8:19._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.62}}
{lalign 8:20._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.57}}
{lalign 8:21._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.52}}
{lalign 8:22._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.47}}
{lalign 8:23._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.42}}
{lalign 8:24._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.37}}
{lalign 8:25._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.32}}
{lalign 8:26._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.27}}
{lalign 8:27._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.22}}
{lalign 8:28._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.17}}
{lalign 8:29._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.12}}
{lalign 8:30._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.07}}
{lalign 8:31._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.02}}
{lalign 8:32._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.03}}
{lalign 8:33._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.08}}
{lalign 8:34._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.13}}
{lalign 8:35._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.18}}
{lalign 8:36._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.23}}
{lalign 8:37._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.28}}
{lalign 8:38._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.33}}
{lalign 8:39._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.38}}
{lalign 8:40._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.43}}
{lalign 8:41._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.48}}
{lalign 8:42._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.53}}
{lalign 8:43._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.58}}
{lalign 8:44._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.63}}
{lalign 8:45._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.68}}
{lalign 8:46._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.73}}
{lalign 8:47._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.78}}
{lalign 8:48._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.83}}
{lalign 8:49._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.88}}
{lalign 8:50._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.93}}
{lalign 8:51._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.98}}
{lalign 8:52._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.03}}
{lalign 8:53._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.08}}
{lalign 8:54._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.13}}
{lalign 8:55._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.18}}
{lalign 8:56._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.23}}
{lalign 8:57._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.28}}
{lalign 8:58._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.33}}
{lalign 8:59._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.38}}
{lalign 8:60._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.43}}
{lalign 8:61._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.48}}
{lalign 8:62._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.53}}
{lalign 8:63._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.58}}
{lalign 8:64._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.63}}
{lalign 8:65._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.68}}
{lalign 8:66._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.73}}
{lalign 8:67._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.78}}
{lalign 8:68._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.83}}
{lalign 8:69._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.88}}
{lalign 8:70._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.93}}
{lalign 8:71._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.98}}
{lalign 8:72._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.03}}
{lalign 8:73._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.08}}
{lalign 8:74._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.13}}
{lalign 8:75._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.18}}
{lalign 8:76._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.23}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .4739528{col 26}{space 2} .0062495{col 37}{space 1}   75.84{col 46}{space 3}0.000{col 54}{space 4}  .461704{col 67}{space 3} .4862016
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .4759721{col 26}{space 2} .0061063{col 37}{space 1}   77.95{col 46}{space 3}0.000{col 54}{space 4}  .464004{col 67}{space 3} .4879402
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{txt}{space 10}5  {c |}{col 14}{res}{space 2} .4820345{col 26}{space 2} .0056866{col 37}{space 1}   84.77{col 46}{space 3}0.000{col 54}{space 4} .4708891{col 67}{space 3}   .49318
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .4840566{col 26}{space 2} .0055505{col 37}{space 1}   87.21{col 46}{space 3}0.000{col 54}{space 4} .4731778{col 67}{space 3} .4949354
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{txt}{space 10}8  {c |}{col 14}{res}{space 2} .4881023{col 26}{space 2} .0052853{col 37}{space 1}   92.35{col 46}{space 3}0.000{col 54}{space 4} .4777433{col 67}{space 3} .4984613
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .4901257{col 26}{space 2} .0051566{col 37}{space 1}   95.05{col 46}{space 3}0.000{col 54}{space 4}  .480019{col 67}{space 3} .5002324
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .4921495{col 26}{space 2} .0050308{col 37}{space 1}   97.83{col 46}{space 3}0.000{col 54}{space 4} .4822894{col 67}{space 3} .5020096
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .4941735{col 26}{space 2} .0049081{col 37}{space 1}  100.69{col 46}{space 3}0.000{col 54}{space 4} .4845539{col 67}{space 3} .5037932
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .4961977{col 26}{space 2} .0047888{col 37}{space 1}  103.62{col 46}{space 3}0.000{col 54}{space 4} .4868118{col 67}{space 3} .5055837
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .4982221{col 26}{space 2} .0046733{col 37}{space 1}  106.61{col 46}{space 3}0.000{col 54}{space 4} .4890626{col 67}{space 3} .5073815
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .5002465{col 26}{space 2} .0045618{col 37}{space 1}  109.66{col 46}{space 3}0.000{col 54}{space 4} .4913056{col 67}{space 3} .5091874
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .5022709{col 26}{space 2} .0044546{col 37}{space 1}  112.75{col 46}{space 3}0.000{col 54}{space 4}   .49354{col 67}{space 3} .5110017
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .5042952{col 26}{space 2} .0043521{col 37}{space 1}  115.87{col 46}{space 3}0.000{col 54}{space 4} .4957652{col 67}{space 3} .5128251
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .5063194{col 26}{space 2} .0042547{col 37}{space 1}  119.00{col 46}{space 3}0.000{col 54}{space 4} .4979804{col 67}{space 3} .5146583
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .5083433{col 26}{space 2} .0041626{col 37}{space 1}  122.12{col 46}{space 3}0.000{col 54}{space 4} .5001847{col 67}{space 3} .5165019
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{txt}{space 9}21  {c |}{col 14}{res}{space 2} .5144133{col 26}{space 2} .0039229{col 37}{space 1}  131.13{col 46}{space 3}0.000{col 54}{space 4} .5067247{col 67}{space 3}  .522102
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .5164358{col 26}{space 2} .0038563{col 37}{space 1}  133.92{col 46}{space 3}0.000{col 54}{space 4} .5088776{col 67}{space 3}  .523994
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .5184577{col 26}{space 2} .0037971{col 37}{space 1}  136.54{col 46}{space 3}0.000{col 54}{space 4} .5110156{col 67}{space 3} .5258999
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .5204791{col 26}{space 2} .0037455{col 37}{space 1}  138.96{col 46}{space 3}0.000{col 54}{space 4} .5131381{col 67}{space 3} .5278201
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .5224997{col 26}{space 2} .0037019{col 37}{space 1}  141.14{col 46}{space 3}0.000{col 54}{space 4} .5152442{col 67}{space 3} .5297553
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .5245197{col 26}{space 2} .0036665{col 37}{space 1}  143.06{col 46}{space 3}0.000{col 54}{space 4} .5173335{col 67}{space 3} .5317059
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .5265388{col 26}{space 2} .0036396{col 37}{space 1}  144.67{col 46}{space 3}0.000{col 54}{space 4} .5194053{col 67}{space 3} .5336723
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .5285571{col 26}{space 2} .0036213{col 37}{space 1}  145.96{col 46}{space 3}0.000{col 54}{space 4} .5214594{col 67}{space 3} .5356547
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .5305744{col 26}{space 2} .0036118{col 37}{space 1}  146.90{col 46}{space 3}0.000{col 54}{space 4} .5234953{col 67}{space 3} .5376534
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .5325907{col 26}{space 2} .0036111{col 37}{space 1}  147.49{col 46}{space 3}0.000{col 54}{space 4} .5255131{col 67}{space 3} .5396684
{txt}{space 9}31  {c |}{col 14}{res}{space 2}  .534606{col 26}{space 2} .0036192{col 37}{space 1}  147.72{col 46}{space 3}0.000{col 54}{space 4} .5275125{col 67}{space 3} .5416994
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .5366201{col 26}{space 2} .0036359{col 37}{space 1}  147.59{col 46}{space 3}0.000{col 54}{space 4} .5294938{col 67}{space 3} .5437464
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .5386331{col 26}{space 2} .0036612{col 37}{space 1}  147.12{col 46}{space 3}0.000{col 54}{space 4} .5314572{col 67}{space 3} .5458089
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .5406447{col 26}{space 2} .0036948{col 37}{space 1}  146.33{col 46}{space 3}0.000{col 54}{space 4}  .533403{col 67}{space 3} .5478864
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .5426551{col 26}{space 2} .0037365{col 37}{space 1}  145.23{col 46}{space 3}0.000{col 54}{space 4} .5353317{col 67}{space 3} .5499785
{txt}{space 9}36  {c |}{col 14}{res}{space 2} .5446641{col 26}{space 2} .0037859{col 37}{space 1}  143.87{col 46}{space 3}0.000{col 54}{space 4} .5372439{col 67}{space 3} .5520843
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .5466716{col 26}{space 2} .0038427{col 37}{space 1}  142.26{col 46}{space 3}0.000{col 54}{space 4}   .53914{col 67}{space 3} .5542032
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .5486776{col 26}{space 2} .0039066{col 37}{space 1}  140.45{col 46}{space 3}0.000{col 54}{space 4} .5410208{col 67}{space 3} .5563343
{txt}{space 9}39  {c |}{col 14}{res}{space 2}  .550682{col 26}{space 2} .0039771{col 37}{space 1}  138.46{col 46}{space 3}0.000{col 54}{space 4} .5428871{col 67}{space 3} .5584769
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .5526848{col 26}{space 2} .0040538{col 37}{space 1}  136.34{col 46}{space 3}0.000{col 54}{space 4} .5447394{col 67}{space 3} .5606302
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .5546858{col 26}{space 2} .0041365{col 37}{space 1}  134.10{col 46}{space 3}0.000{col 54}{space 4} .5465785{col 67}{space 3} .5627931
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .5566851{col 26}{space 2} .0042245{col 37}{space 1}  131.77{col 46}{space 3}0.000{col 54}{space 4} .5484052{col 67}{space 3}  .564965
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .5586826{col 26}{space 2} .0043177{col 37}{space 1}  129.39{col 46}{space 3}0.000{col 54}{space 4} .5502201{col 67}{space 3}  .567145
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .5606782{col 26}{space 2} .0044155{col 37}{space 1}  126.98{col 46}{space 3}0.000{col 54}{space 4}  .552024{col 67}{space 3} .5693323
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .5626718{col 26}{space 2} .0045176{col 37}{space 1}  124.55{col 46}{space 3}0.000{col 54}{space 4} .5538174{col 67}{space 3} .5715262
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .5646633{col 26}{space 2} .0046238{col 37}{space 1}  122.12{col 46}{space 3}0.000{col 54}{space 4} .5556009{col 67}{space 3} .5737257
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .5666528{col 26}{space 2} .0047335{col 37}{space 1}  119.71{col 46}{space 3}0.000{col 54}{space 4} .5573753{col 67}{space 3} .5759304
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .5686402{col 26}{space 2} .0048467{col 37}{space 1}  117.33{col 46}{space 3}0.000{col 54}{space 4} .5591409{col 67}{space 3} .5781395
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .5706253{col 26}{space 2} .0049629{col 37}{space 1}  114.98{col 46}{space 3}0.000{col 54}{space 4} .5608983{col 67}{space 3} .5803524
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .5726082{col 26}{space 2} .0050818{col 37}{space 1}  112.68{col 46}{space 3}0.000{col 54}{space 4}  .562648{col 67}{space 3} .5825684
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .5745887{col 26}{space 2} .0052033{col 37}{space 1}  110.43{col 46}{space 3}0.000{col 54}{space 4} .5643904{col 67}{space 3} .5847871
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .5765669{col 26}{space 2} .0053271{col 37}{space 1}  108.23{col 46}{space 3}0.000{col 54}{space 4} .5661259{col 67}{space 3} .5870079
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .5785426{col 26}{space 2}  .005453{col 37}{space 1}  106.10{col 46}{space 3}0.000{col 54}{space 4} .5678549{col 67}{space 3} .5892303
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .5805158{col 26}{space 2} .0055808{col 37}{space 1}  104.02{col 46}{space 3}0.000{col 54}{space 4} .5695777{col 67}{space 3} .5914539
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .5824864{col 26}{space 2} .0057102{col 37}{space 1}  102.01{col 46}{space 3}0.000{col 54}{space 4} .5712946{col 67}{space 3} .5936782
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .5844544{col 26}{space 2} .0058412{col 37}{space 1}  100.06{col 46}{space 3}0.000{col 54}{space 4} .5730059{col 67}{space 3} .5959029
{txt}{space 9}57  {c |}{col 14}{res}{space 2} .5864197{col 26}{space 2} .0059735{col 37}{space 1}   98.17{col 46}{space 3}0.000{col 54}{space 4} .5747119{col 67}{space 3} .5981275
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .5883822{col 26}{space 2}  .006107{col 37}{space 1}   96.35{col 46}{space 3}0.000{col 54}{space 4} .5764128{col 67}{space 3} .6003517
{txt}{space 9}59  {c |}{col 14}{res}{space 2}  .590342{col 26}{space 2} .0062415{col 37}{space 1}   94.58{col 46}{space 3}0.000{col 54}{space 4} .5781088{col 67}{space 3} .6025752
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .5922988{col 26}{space 2}  .006377{col 37}{space 1}   92.88{col 46}{space 3}0.000{col 54}{space 4} .5798001{col 67}{space 3} .6047976
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .5942528{col 26}{space 2} .0065133{col 37}{space 1}   91.24{col 46}{space 3}0.000{col 54}{space 4} .5814869{col 67}{space 3} .6070187
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .5962038{col 26}{space 2} .0066503{col 37}{space 1}   89.65{col 46}{space 3}0.000{col 54}{space 4} .5831694{col 67}{space 3} .6092382
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .5981517{col 26}{space 2} .0067879{col 37}{space 1}   88.12{col 46}{space 3}0.000{col 54}{space 4} .5848476{col 67}{space 3} .6114558
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .6000966{col 26}{space 2}  .006926{col 37}{space 1}   86.64{col 46}{space 3}0.000{col 54}{space 4} .5865218{col 67}{space 3} .6136713
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .6020382{col 26}{space 2} .0070645{col 37}{space 1}   85.22{col 46}{space 3}0.000{col 54}{space 4} .5881921{col 67}{space 3} .6158844
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .6039767{col 26}{space 2} .0072033{col 37}{space 1}   83.85{col 46}{space 3}0.000{col 54}{space 4} .5898585{col 67}{space 3}  .618095
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .6059119{col 26}{space 2} .0073424{col 37}{space 1}   82.52{col 46}{space 3}0.000{col 54}{space 4} .5915211{col 67}{space 3} .6203028
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .6078439{col 26}{space 2} .0074816{col 37}{space 1}   81.25{col 46}{space 3}0.000{col 54}{space 4} .5931802{col 67}{space 3} .6225075
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .6097724{col 26}{space 2} .0076209{col 37}{space 1}   80.01{col 46}{space 3}0.000{col 54}{space 4} .5948356{col 67}{space 3} .6247092
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .6116975{col 26}{space 2} .0077603{col 37}{space 1}   78.82{col 46}{space 3}0.000{col 54}{space 4} .5964876{col 67}{space 3} .6269074
{txt}{space 9}71  {c |}{col 14}{res}{space 2} .6136191{col 26}{space 2} .0078996{col 37}{space 1}   77.68{col 46}{space 3}0.000{col 54}{space 4} .5981361{col 67}{space 3} .6291021
{txt}{space 9}72  {c |}{col 14}{res}{space 2} .6155372{col 26}{space 2} .0080389{col 37}{space 1}   76.57{col 46}{space 3}0.000{col 54}{space 4} .5997813{col 67}{space 3} .6312931
{txt}{space 9}73  {c |}{col 14}{res}{space 2} .6174517{col 26}{space 2}  .008178{col 37}{space 1}   75.50{col 46}{space 3}0.000{col 54}{space 4} .6014232{col 67}{space 3} .6334803
{txt}{space 9}74  {c |}{col 14}{res}{space 2} .6193626{col 26}{space 2} .0083169{col 37}{space 1}   74.47{col 46}{space 3}0.000{col 54}{space 4} .6030618{col 67}{space 3} .6356635
{txt}{space 9}75  {c |}{col 14}{res}{space 2} .6212698{col 26}{space 2} .0084556{col 37}{space 1}   73.47{col 46}{space 3}0.000{col 54}{space 4} .6046972{col 67}{space 3} .6378424
{txt}{space 9}76  {c |}{col 14}{res}{space 2} .6231732{col 26}{space 2}  .008594{col 37}{space 1}   72.51{col 46}{space 3}0.000{col 54}{space 4} .6063293{col 67}{space 3} .6400171
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}.      marginsplot , recast(line) recastci(rline) ci1opts(fintensity(50) lpattern(dot)) xti(Risk of automation) yti("Predicted probability (95% CI)") ti("Job dissatisfaction")  saving("Figure/difficult.gph", replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:rti}{p_end}
{res}{txt}{p 0 4 2}
(file {bf}
Figure/difficult.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Figure/difficult.gph} saved
{com}. {c )-}
. 
.          
. * W_satisfaction
. {c -(}
. gen dissatisfied=1 if W_satisfaction==5 | W_satisfaction==6 | W_satisfaction==7
{txt}(202,467 missing values generated)
{com}. replace dissatisfied=0 if W_satisfaction==1 | W_satisfaction==2 | W_satisfaction==3 | W_satisfaction==4
{txt}(25,253 real changes made)
{com}. 
. 
. logit dissatisfied rti [pweight=weight]

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-5231.0894}  
Iteration 1:{space 3}log pseudolikelihood = {res:-5216.5255}  
Iteration 2:{space 3}log pseudolikelihood = {res:-5216.3905}  
Iteration 3:{space 3}log pseudolikelihood = {res:-5216.3905}  
{res}
{txt}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:21,845}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:27.37}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}Log pseudolikelihood = {res:-5216.3905}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0028}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}dissatisfied{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rti {c |}{col 14}{res}{space 2} .1403051{col 26}{space 2} .0268173{col 37}{space 1}    5.23{col 46}{space 3}0.000{col 54}{space 4} .0877442{col 67}{space 3} .1928661
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-2.663969{col 26}{space 2} .0298265{col 37}{space 1}  -89.32{col 46}{space 3}0.000{col 54}{space 4}-2.722428{col 67}{space 3} -2.60551
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. 
.  margins, atmeans at(rti=(-1.52(0.05)2.24)) 
{res}
{txt}Adjusted predictions{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:21,845}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(dissatisfied), predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.52}}
{lalign 8:2._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.47}}
{lalign 8:3._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.42}}
{lalign 8:4._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.37}}
{lalign 8:5._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.32}}
{lalign 8:6._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.27}}
{lalign 8:7._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.22}}
{lalign 8:8._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.17}}
{lalign 8:9._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.12}}
{lalign 8:10._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.07}}
{lalign 8:11._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.02}}
{lalign 8:12._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.97}}
{lalign 8:13._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.92}}
{lalign 8:14._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.87}}
{lalign 8:15._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.82}}
{lalign 8:16._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.77}}
{lalign 8:17._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.72}}
{lalign 8:18._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.67}}
{lalign 8:19._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.62}}
{lalign 8:20._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.57}}
{lalign 8:21._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.52}}
{lalign 8:22._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.47}}
{lalign 8:23._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.42}}
{lalign 8:24._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.37}}
{lalign 8:25._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.32}}
{lalign 8:26._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.27}}
{lalign 8:27._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.22}}
{lalign 8:28._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.17}}
{lalign 8:29._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.12}}
{lalign 8:30._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.07}}
{lalign 8:31._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.02}}
{lalign 8:32._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.03}}
{lalign 8:33._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.08}}
{lalign 8:34._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.13}}
{lalign 8:35._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.18}}
{lalign 8:36._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.23}}
{lalign 8:37._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.28}}
{lalign 8:38._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.33}}
{lalign 8:39._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.38}}
{lalign 8:40._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.43}}
{lalign 8:41._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.48}}
{lalign 8:42._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.53}}
{lalign 8:43._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.58}}
{lalign 8:44._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.63}}
{lalign 8:45._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.68}}
{lalign 8:46._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.73}}
{lalign 8:47._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.78}}
{lalign 8:48._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.83}}
{lalign 8:49._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.88}}
{lalign 8:50._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.93}}
{lalign 8:51._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.98}}
{lalign 8:52._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.03}}
{lalign 8:53._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.08}}
{lalign 8:54._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.13}}
{lalign 8:55._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.18}}
{lalign 8:56._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.23}}
{lalign 8:57._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.28}}
{lalign 8:58._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.33}}
{lalign 8:59._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.38}}
{lalign 8:60._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.43}}
{lalign 8:61._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.48}}
{lalign 8:62._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.53}}
{lalign 8:63._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.58}}
{lalign 8:64._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.63}}
{lalign 8:65._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.68}}
{lalign 8:66._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.73}}
{lalign 8:67._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.78}}
{lalign 8:68._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.83}}
{lalign 8:69._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.88}}
{lalign 8:70._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.93}}
{lalign 8:71._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.98}}
{lalign 8:72._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.03}}
{lalign 8:73._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.08}}
{lalign 8:74._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.13}}
{lalign 8:75._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.18}}
{lalign 8:76._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.23}}

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{txt}{space 9}75  {c |}{col 14}{res}{space 2} .0864238{col 26}{space 2}  .005048{col 37}{space 1}   17.12{col 46}{space 3}0.000{col 54}{space 4} .0765298{col 67}{space 3} .0963177
{txt}{space 9}76  {c |}{col 14}{res}{space 2} .0869793{col 26}{space 2} .0051719{col 37}{space 1}   16.82{col 46}{space 3}0.000{col 54}{space 4} .0768426{col 67}{space 3} .0971159
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}.      marginsplot , recast(line) recastci(rline) ci1opts(fintensity(50) lpattern(dot)) xti(Risk of automation) yti("Predicted probability (95% CI)") ti("Job dissatisfaction")  saving("Figure/dissatisfied.gph", replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:rti}{p_end}
{res}{txt}{p 0 4 2}
(file {bf}
Figure/dissatisfied.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Figure/dissatisfied.gph} saved
{com}. {c )-}        
. 
.          
. * W_losing
. {c -(}
. gen W_losing2=1 if W_losing==1 | W_losing==2 | W_losing==3
{txt}(189,775 missing values generated)
{com}. replace W_losing2=0 if W_losing==4 
{txt}(12,463 real changes made)
{com}. 
. 
. logit W_losing2   rti [pweight=weight]

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-14884.364}  
Iteration 1:{space 3}log pseudolikelihood = {res:-14852.506}  
Iteration 2:{space 3}log pseudolikelihood = {res:-14852.502}  
Iteration 3:{space 3}log pseudolikelihood = {res:-14852.502}  
{res}
{txt}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:21,764}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:56.57}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}Log pseudolikelihood = {res:-14852.502}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0021}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   W_losing2{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rti {c |}{col 14}{res}{space 2} .1077349{col 26}{space 2} .0143236{col 37}{space 1}    7.52{col 46}{space 3}0.000{col 54}{space 4} .0796612{col 67}{space 3} .1358087
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2321113{col 26}{space 2} .0144658{col 37}{space 1}   16.05{col 46}{space 3}0.000{col 54}{space 4} .2037589{col 67}{space 3} .2604638
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. 
.  margins, atmeans at(rti=(-1.52(0.05)2.24)) 
{res}
{txt}Adjusted predictions{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:21,764}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(W_losing2), predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.52}}
{lalign 8:2._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.47}}
{lalign 8:3._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.42}}
{lalign 8:4._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.37}}
{lalign 8:5._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.32}}
{lalign 8:6._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.27}}
{lalign 8:7._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.22}}
{lalign 8:8._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.17}}
{lalign 8:9._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.12}}
{lalign 8:10._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.07}}
{lalign 8:11._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.02}}
{lalign 8:12._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.97}}
{lalign 8:13._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.92}}
{lalign 8:14._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.87}}
{lalign 8:15._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.82}}
{lalign 8:16._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.77}}
{lalign 8:17._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.72}}
{lalign 8:18._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.67}}
{lalign 8:19._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.62}}
{lalign 8:20._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.57}}
{lalign 8:21._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.52}}
{lalign 8:22._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.47}}
{lalign 8:23._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.42}}
{lalign 8:24._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.37}}
{lalign 8:25._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.32}}
{lalign 8:26._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.27}}
{lalign 8:27._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.22}}
{lalign 8:28._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.17}}
{lalign 8:29._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.12}}
{lalign 8:30._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.07}}
{lalign 8:31._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.02}}
{lalign 8:32._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.03}}
{lalign 8:33._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.08}}
{lalign 8:34._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.13}}
{lalign 8:35._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.18}}
{lalign 8:36._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.23}}
{lalign 8:37._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.28}}
{lalign 8:38._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.33}}
{lalign 8:39._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.38}}
{lalign 8:40._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.43}}
{lalign 8:41._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.48}}
{lalign 8:42._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.53}}
{lalign 8:43._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.58}}
{lalign 8:44._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.63}}
{lalign 8:45._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.68}}
{lalign 8:46._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.73}}
{lalign 8:47._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.78}}
{lalign 8:48._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.83}}
{lalign 8:49._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.88}}
{lalign 8:50._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.93}}
{lalign 8:51._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.98}}
{lalign 8:52._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.03}}
{lalign 8:53._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.08}}
{lalign 8:54._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.13}}
{lalign 8:55._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.18}}
{lalign 8:56._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.23}}
{lalign 8:57._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.28}}
{lalign 8:58._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.33}}
{lalign 8:59._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.38}}
{lalign 8:60._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.43}}
{lalign 8:61._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.48}}
{lalign 8:62._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.53}}
{lalign 8:63._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.58}}
{lalign 8:64._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.63}}
{lalign 8:65._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.68}}
{lalign 8:66._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.73}}
{lalign 8:67._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.78}}
{lalign 8:68._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.83}}
{lalign 8:69._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.88}}
{lalign 8:70._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.93}}
{lalign 8:71._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.98}}
{lalign 8:72._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.03}}
{lalign 8:73._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.08}}
{lalign 8:74._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.13}}
{lalign 8:75._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.18}}
{lalign 8:76._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.23}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .5170819{col 26}{space 2} .0062078{col 37}{space 1}   83.30{col 46}{space 3}0.000{col 54}{space 4} .5049149{col 67}{space 3} .5292489
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .5184269{col 26}{space 2} .0060617{col 37}{space 1}   85.53{col 46}{space 3}0.000{col 54}{space 4} .5065463{col 67}{space 3} .5303075
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .5197716{col 26}{space 2} .0059174{col 37}{space 1}   87.84{col 46}{space 3}0.000{col 54}{space 4} .5081738{col 67}{space 3} .5313694
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .5211161{col 26}{space 2}  .005775{col 37}{space 1}   90.24{col 46}{space 3}0.000{col 54}{space 4} .5097972{col 67}{space 3} .5324349
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .5224602{col 26}{space 2} .0056348{col 37}{space 1}   92.72{col 46}{space 3}0.000{col 54}{space 4} .5114161{col 67}{space 3} .5335042
{txt}{space 10}6  {c |}{col 14}{res}{space 2}  .523804{col 26}{space 2} .0054969{col 37}{space 1}   95.29{col 46}{space 3}0.000{col 54}{space 4} .5130303{col 67}{space 3} .5345777
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .5251474{col 26}{space 2} .0053614{col 37}{space 1}   97.95{col 46}{space 3}0.000{col 54}{space 4} .5146392{col 67}{space 3} .5356557
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .5264905{col 26}{space 2} .0052286{col 37}{space 1}  100.69{col 46}{space 3}0.000{col 54}{space 4} .5162426{col 67}{space 3} .5367385
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .5278333{col 26}{space 2} .0050987{col 37}{space 1}  103.52{col 46}{space 3}0.000{col 54}{space 4}   .51784{col 67}{space 3} .5378266
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .5291756{col 26}{space 2} .0049719{col 37}{space 1}  106.43{col 46}{space 3}0.000{col 54}{space 4} .5194309{col 67}{space 3} .5389203
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .5305175{col 26}{space 2} .0048484{col 37}{space 1}  109.42{col 46}{space 3}0.000{col 54}{space 4} .5210148{col 67}{space 3} .5400201
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .5318589{col 26}{space 2} .0047285{col 37}{space 1}  112.48{col 46}{space 3}0.000{col 54}{space 4} .5225912{col 67}{space 3} .5411266
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .5331999{col 26}{space 2} .0046125{col 37}{space 1}  115.60{col 46}{space 3}0.000{col 54}{space 4} .5241596{col 67}{space 3} .5422401
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .5345404{col 26}{space 2} .0045006{col 37}{space 1}  118.77{col 46}{space 3}0.000{col 54}{space 4} .5257194{col 67}{space 3} .5433614
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .5358804{col 26}{space 2} .0043932{col 37}{space 1}  121.98{col 46}{space 3}0.000{col 54}{space 4} .5272698{col 67}{space 3}  .544491
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .5372199{col 26}{space 2} .0042907{col 37}{space 1}  125.21{col 46}{space 3}0.000{col 54}{space 4} .5288103{col 67}{space 3} .5456294
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .5385588{col 26}{space 2} .0041932{col 37}{space 1}  128.43{col 46}{space 3}0.000{col 54}{space 4} .5303402{col 67}{space 3} .5467775
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .5398972{col 26}{space 2} .0041013{col 37}{space 1}  131.64{col 46}{space 3}0.000{col 54}{space 4} .5318587{col 67}{space 3} .5479357
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .5412351{col 26}{space 2} .0040153{col 37}{space 1}  134.79{col 46}{space 3}0.000{col 54}{space 4} .5333652{col 67}{space 3} .5491049
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .5425723{col 26}{space 2} .0039355{col 37}{space 1}  137.86{col 46}{space 3}0.000{col 54}{space 4} .5348587{col 67}{space 3} .5502858
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .5439089{col 26}{space 2} .0038624{col 37}{space 1}  140.82{col 46}{space 3}0.000{col 54}{space 4} .5363387{col 67}{space 3} .5514791
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .5452449{col 26}{space 2} .0037962{col 37}{space 1}  143.63{col 46}{space 3}0.000{col 54}{space 4} .5378044{col 67}{space 3} .5526854
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .5465802{col 26}{space 2} .0037374{col 37}{space 1}  146.25{col 46}{space 3}0.000{col 54}{space 4}  .539255{col 67}{space 3} .5539054
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .5479149{col 26}{space 2} .0036862{col 37}{space 1}  148.64{col 46}{space 3}0.000{col 54}{space 4}   .54069{col 67}{space 3} .5551398
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .5492488{col 26}{space 2} .0036431{col 37}{space 1}  150.77{col 46}{space 3}0.000{col 54}{space 4} .5421086{col 67}{space 3} .5563891
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .5505821{col 26}{space 2} .0036081{col 37}{space 1}  152.60{col 46}{space 3}0.000{col 54}{space 4} .5435103{col 67}{space 3} .5576539
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .5519146{col 26}{space 2} .0035816{col 37}{space 1}  154.10{col 46}{space 3}0.000{col 54}{space 4} .5448948{col 67}{space 3} .5589345
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .5532464{col 26}{space 2} .0035637{col 37}{space 1}  155.24{col 46}{space 3}0.000{col 54}{space 4} .5462617{col 67}{space 3} .5602312
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .5545775{col 26}{space 2} .0035546{col 37}{space 1}  156.02{col 46}{space 3}0.000{col 54}{space 4} .5476107{col 67}{space 3} .5615443
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .5559077{col 26}{space 2} .0035541{col 37}{space 1}  156.41{col 46}{space 3}0.000{col 54}{space 4} .5489417{col 67}{space 3} .5628737
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .5572372{col 26}{space 2} .0035624{col 37}{space 1}  156.42{col 46}{space 3}0.000{col 54}{space 4} .5502549{col 67}{space 3} .5642194
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .5585658{col 26}{space 2} .0035794{col 37}{space 1}  156.05{col 46}{space 3}0.000{col 54}{space 4} .5515504{col 67}{space 3} .5655812
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .5598936{col 26}{space 2} .0036048{col 37}{space 1}  155.32{col 46}{space 3}0.000{col 54}{space 4} .5528284{col 67}{space 3} .5669588
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .5612205{col 26}{space 2} .0036384{col 37}{space 1}  154.25{col 46}{space 3}0.000{col 54}{space 4} .5540893{col 67}{space 3} .5683517
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .5625466{col 26}{space 2} .0036801{col 37}{space 1}  152.86{col 46}{space 3}0.000{col 54}{space 4} .5553337{col 67}{space 3} .5697594
{txt}{space 9}36  {c |}{col 14}{res}{space 2} .5638717{col 26}{space 2} .0037295{col 37}{space 1}  151.19{col 46}{space 3}0.000{col 54}{space 4} .5565621{col 67}{space 3} .5711813
{txt}{space 9}37  {c |}{col 14}{res}{space 2}  .565196{col 26}{space 2} .0037862{col 37}{space 1}  149.28{col 46}{space 3}0.000{col 54}{space 4} .5577752{col 67}{space 3} .5726167
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .5665193{col 26}{space 2} .0038499{col 37}{space 1}  147.15{col 46}{space 3}0.000{col 54}{space 4} .5589737{col 67}{space 3} .5740649
{txt}{space 9}39  {c |}{col 14}{res}{space 2} .5678417{col 26}{space 2} .0039202{col 37}{space 1}  144.85{col 46}{space 3}0.000{col 54}{space 4} .5601582{col 67}{space 3} .5755251
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .5691631{col 26}{space 2} .0039968{col 37}{space 1}  142.41{col 46}{space 3}0.000{col 54}{space 4} .5613296{col 67}{space 3} .5769966
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .5704835{col 26}{space 2} .0040791{col 37}{space 1}  139.85{col 46}{space 3}0.000{col 54}{space 4} .5624885{col 67}{space 3} .5784785
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .5718029{col 26}{space 2}  .004167{col 37}{space 1}  137.22{col 46}{space 3}0.000{col 54}{space 4} .5636358{col 67}{space 3} .5799701
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .5731213{col 26}{space 2} .0042599{col 37}{space 1}  134.54{col 46}{space 3}0.000{col 54}{space 4} .5647721{col 67}{space 3} .5814706
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .5744387{col 26}{space 2} .0043575{col 37}{space 1}  131.83{col 46}{space 3}0.000{col 54}{space 4} .5658981{col 67}{space 3} .5829792
{txt}{space 9}45  {c |}{col 14}{res}{space 2}  .575755{col 26}{space 2} .0044595{col 37}{space 1}  129.11{col 46}{space 3}0.000{col 54}{space 4} .5670146{col 67}{space 3} .5844954
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .5770702{col 26}{space 2} .0045655{col 37}{space 1}  126.40{col 46}{space 3}0.000{col 54}{space 4}  .568122{col 67}{space 3} .5860184
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .5783844{col 26}{space 2} .0046752{col 37}{space 1}  123.71{col 46}{space 3}0.000{col 54}{space 4} .5692211{col 67}{space 3} .5875476
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .5796974{col 26}{space 2} .0047883{col 37}{space 1}  121.06{col 46}{space 3}0.000{col 54}{space 4} .5703124{col 67}{space 3} .5890824
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .5810093{col 26}{space 2} .0049046{col 37}{space 1}  118.46{col 46}{space 3}0.000{col 54}{space 4} .5713964{col 67}{space 3} .5906222
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .5823201{col 26}{space 2} .0050238{col 37}{space 1}  115.91{col 46}{space 3}0.000{col 54}{space 4} .5724736{col 67}{space 3} .5921665
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .5836297{col 26}{space 2} .0051456{col 37}{space 1}  113.42{col 46}{space 3}0.000{col 54}{space 4} .5735445{col 67}{space 3} .5937148
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .5849381{col 26}{space 2} .0052698{col 37}{space 1}  111.00{col 46}{space 3}0.000{col 54}{space 4} .5746094{col 67}{space 3} .5952667
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .5862453{col 26}{space 2} .0053963{col 37}{space 1}  108.64{col 46}{space 3}0.000{col 54}{space 4} .5756688{col 67}{space 3} .5968218
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .5875513{col 26}{space 2} .0055247{col 37}{space 1}  106.35{col 46}{space 3}0.000{col 54}{space 4} .5767231{col 67}{space 3} .5983796
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .5888561{col 26}{space 2}  .005655{col 37}{space 1}  104.13{col 46}{space 3}0.000{col 54}{space 4} .5777724{col 67}{space 3} .5999398
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .5901596{col 26}{space 2}  .005787{col 37}{space 1}  101.98{col 46}{space 3}0.000{col 54}{space 4} .5788173{col 67}{space 3}  .601502
{txt}{space 9}57  {c |}{col 14}{res}{space 2} .5914619{col 26}{space 2} .0059205{col 37}{space 1}   99.90{col 46}{space 3}0.000{col 54}{space 4} .5798578{col 67}{space 3} .6030659
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .5927629{col 26}{space 2} .0060555{col 37}{space 1}   97.89{col 46}{space 3}0.000{col 54}{space 4} .5808944{col 67}{space 3} .6046314
{txt}{space 9}59  {c |}{col 14}{res}{space 2} .5940625{col 26}{space 2} .0061916{col 37}{space 1}   95.95{col 46}{space 3}0.000{col 54}{space 4} .5819272{col 67}{space 3} .6061979
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .5953609{col 26}{space 2}  .006329{col 37}{space 1}   94.07{col 46}{space 3}0.000{col 54}{space 4} .5829564{col 67}{space 3} .6077654
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .5966579{col 26}{space 2} .0064673{col 37}{space 1}   92.26{col 46}{space 3}0.000{col 54}{space 4} .5839822{col 67}{space 3} .6093336
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .5979536{col 26}{space 2} .0066066{col 37}{space 1}   90.51{col 46}{space 3}0.000{col 54}{space 4} .5850049{col 67}{space 3} .6109023
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .5992479{col 26}{space 2} .0067467{col 37}{space 1}   88.82{col 46}{space 3}0.000{col 54}{space 4} .5860246{col 67}{space 3} .6124713
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .6005409{col 26}{space 2} .0068876{col 37}{space 1}   87.19{col 46}{space 3}0.000{col 54}{space 4} .5870414{col 67}{space 3} .6140403
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .6018324{col 26}{space 2} .0070291{col 37}{space 1}   85.62{col 46}{space 3}0.000{col 54}{space 4} .5880556{col 67}{space 3} .6156092
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .6031225{col 26}{space 2} .0071713{col 37}{space 1}   84.10{col 46}{space 3}0.000{col 54}{space 4} .5890671{col 67}{space 3} .6171779
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .6044112{col 26}{space 2} .0073139{col 37}{space 1}   82.64{col 46}{space 3}0.000{col 54}{space 4} .5900762{col 67}{space 3} .6187462
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .6056984{col 26}{space 2}  .007457{col 37}{space 1}   81.23{col 46}{space 3}0.000{col 54}{space 4}  .591083{col 67}{space 3} .6203139
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .6069842{col 26}{space 2} .0076005{col 37}{space 1}   79.86{col 46}{space 3}0.000{col 54}{space 4} .5920875{col 67}{space 3} .6218809
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .6082685{col 26}{space 2} .0077443{col 37}{space 1}   78.54{col 46}{space 3}0.000{col 54}{space 4} .5930899{col 67}{space 3} .6234471
{txt}{space 9}71  {c |}{col 14}{res}{space 2} .6095513{col 26}{space 2} .0078885{col 37}{space 1}   77.27{col 46}{space 3}0.000{col 54}{space 4} .5940902{col 67}{space 3} .6250124
{txt}{space 9}72  {c |}{col 14}{res}{space 2} .6108326{col 26}{space 2} .0080328{col 37}{space 1}   76.04{col 46}{space 3}0.000{col 54}{space 4} .5950886{col 67}{space 3} .6265766
{txt}{space 9}73  {c |}{col 14}{res}{space 2} .6121123{col 26}{space 2} .0081773{col 37}{space 1}   74.85{col 46}{space 3}0.000{col 54}{space 4}  .596085{col 67}{space 3} .6281396
{txt}{space 9}74  {c |}{col 14}{res}{space 2} .6133905{col 26}{space 2}  .008322{col 37}{space 1}   73.71{col 46}{space 3}0.000{col 54}{space 4} .5970797{col 67}{space 3} .6297014
{txt}{space 9}75  {c |}{col 14}{res}{space 2} .6146672{col 26}{space 2} .0084668{col 37}{space 1}   72.60{col 46}{space 3}0.000{col 54}{space 4} .5980725{col 67}{space 3} .6312618
{txt}{space 9}76  {c |}{col 14}{res}{space 2} .6159422{col 26}{space 2} .0086117{col 37}{space 1}   71.52{col 46}{space 3}0.000{col 54}{space 4} .5990637{col 67}{space 3} .6328208
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}.      marginsplot , recast(line) recastci(rline) ci1opts(fintensity(50) lpattern(dot)) xti(Risk of automation) yti("Predicted probability (95% CI)") ti("Concerns about losing job") saving("Figure/losing.gph", replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:rti}{p_end}
{res}{txt}{p 0 4 2}
(file {bf}
Figure/losing.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Figure/losing.gph} saved
{com}. {c )-}
. 
. * W_jobsec
. {c -(}
. gen W_jobsec2=1 if W_jobsec==1 | W_jobsec==2
{txt}(174,934 missing values generated)
{com}. replace W_jobsec2=0 if W_jobsec==3 | W_jobsec==4 | W_jobsec==5
{txt}(2,002 real changes made)
{com}. 
. 
. logit W_jobsec2 rti [pweight=weight] 

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-5519.7187}  
Iteration 1:{space 3}log pseudolikelihood = {res:-5467.2382}  
Iteration 2:{space 3}log pseudolikelihood = {res:-5466.2802}  
Iteration 3:{space 3}log pseudolikelihood = {res:-5466.2799}  
{res}
{txt}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:24,766}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:81.55}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}Log pseudolikelihood = {res:-5466.2799}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0097}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   W_jobsec2{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rti {c |}{col 14}{res}{space 2} .3036476{col 26}{space 2} .0336245{col 37}{space 1}    9.03{col 46}{space 3}0.000{col 54}{space 4} .2377449{col 67}{space 3} .3695504
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.828694{col 26}{space 2} .0300665{col 37}{space 1}   94.08{col 46}{space 3}0.000{col 54}{space 4} 2.769765{col 67}{space 3} 2.887624
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}.  margins, atmeans at(rti=(-1.52(0.05)2.24)) 
{res}
{txt}Adjusted predictions{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:24,766}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(W_jobsec2), predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.52}}
{lalign 8:2._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.47}}
{lalign 8:3._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.42}}
{lalign 8:4._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.37}}
{lalign 8:5._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.32}}
{lalign 8:6._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.27}}
{lalign 8:7._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.22}}
{lalign 8:8._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.17}}
{lalign 8:9._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.12}}
{lalign 8:10._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.07}}
{lalign 8:11._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.02}}
{lalign 8:12._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.97}}
{lalign 8:13._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.92}}
{lalign 8:14._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.87}}
{lalign 8:15._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.82}}
{lalign 8:16._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.77}}
{lalign 8:17._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.72}}
{lalign 8:18._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.67}}
{lalign 8:19._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.62}}
{lalign 8:20._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.57}}
{lalign 8:21._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.52}}
{lalign 8:22._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.47}}
{lalign 8:23._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.42}}
{lalign 8:24._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.37}}
{lalign 8:25._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.32}}
{lalign 8:26._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.27}}
{lalign 8:27._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.22}}
{lalign 8:28._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.17}}
{lalign 8:29._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.12}}
{lalign 8:30._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.07}}
{lalign 8:31._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.02}}
{lalign 8:32._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.03}}
{lalign 8:33._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.08}}
{lalign 8:34._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.13}}
{lalign 8:35._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.18}}
{lalign 8:36._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.23}}
{lalign 8:37._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.28}}
{lalign 8:38._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.33}}
{lalign 8:39._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.38}}
{lalign 8:40._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.43}}
{lalign 8:41._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.48}}
{lalign 8:42._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.53}}
{lalign 8:43._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.58}}
{lalign 8:44._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.63}}
{lalign 8:45._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.68}}
{lalign 8:46._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.73}}
{lalign 8:47._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.78}}
{lalign 8:48._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.83}}
{lalign 8:49._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.88}}
{lalign 8:50._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.93}}
{lalign 8:51._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.98}}
{lalign 8:52._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.03}}
{lalign 8:53._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.08}}
{lalign 8:54._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.13}}
{lalign 8:55._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.18}}
{lalign 8:56._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.23}}
{lalign 8:57._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.28}}
{lalign 8:58._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.33}}
{lalign 8:59._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.38}}
{lalign 8:60._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.43}}
{lalign 8:61._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.48}}
{lalign 8:62._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.53}}
{lalign 8:63._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.58}}
{lalign 8:64._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.63}}
{lalign 8:65._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.68}}
{lalign 8:66._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.73}}
{lalign 8:67._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.78}}
{lalign 8:68._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.83}}
{lalign 8:69._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.88}}
{lalign 8:70._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.93}}
{lalign 8:71._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.98}}
{lalign 8:72._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.03}}
{lalign 8:73._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.08}}
{lalign 8:74._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.13}}
{lalign 8:75._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.18}}
{lalign 8:76._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.23}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .9142878{col 26}{space 2}  .003851{col 37}{space 1}  237.42{col 46}{space 3}0.000{col 54}{space 4}   .90674{col 67}{space 3} .9218356
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .9154701{col 26}{space 2} .0036967{col 37}{space 1}  247.64{col 46}{space 3}0.000{col 54}{space 4} .9082246{col 67}{space 3} .9227156
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .9166376{col 26}{space 2} .0035472{col 37}{space 1}  258.41{col 46}{space 3}0.000{col 54}{space 4} .9096852{col 67}{space 3} .9235899
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .9177904{col 26}{space 2} .0034024{col 37}{space 1}  269.75{col 46}{space 3}0.000{col 54}{space 4} .9111219{col 67}{space 3} .9244589
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .9189287{col 26}{space 2} .0032623{col 37}{space 1}  281.68{col 46}{space 3}0.000{col 54}{space 4} .9125346{col 67}{space 3} .9253227
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .9200526{col 26}{space 2} .0031271{col 37}{space 1}  294.22{col 46}{space 3}0.000{col 54}{space 4} .9139235{col 67}{space 3} .9261816
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .9211622{col 26}{space 2} .0029968{col 37}{space 1}  307.38{col 46}{space 3}0.000{col 54}{space 4} .9152886{col 67}{space 3} .9270359
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .9222578{col 26}{space 2} .0028715{col 37}{space 1}  321.18{col 46}{space 3}0.000{col 54}{space 4} .9166297{col 67}{space 3} .9278858
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .9233394{col 26}{space 2} .0027513{col 37}{space 1}  335.61{col 46}{space 3}0.000{col 54}{space 4}  .917947{col 67}{space 3} .9287318
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .9244072{col 26}{space 2} .0026362{col 37}{space 1}  350.66{col 46}{space 3}0.000{col 54}{space 4} .9192404{col 67}{space 3}  .929574
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .9254613{col 26}{space 2} .0025264{col 37}{space 1}  366.32{col 46}{space 3}0.000{col 54}{space 4} .9205097{col 67}{space 3} .9304129
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .9265019{col 26}{space 2}  .002422{col 37}{space 1}  382.54{col 46}{space 3}0.000{col 54}{space 4} .9217549{col 67}{space 3} .9312488
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .9275291{col 26}{space 2} .0023231{col 37}{space 1}  399.27{col 46}{space 3}0.000{col 54}{space 4} .9229759{col 67}{space 3} .9320822
{txt}{space 9}14  {c |}{col 14}{res}{space 2}  .928543{col 26}{space 2} .0022298{col 37}{space 1}  416.42{col 46}{space 3}0.000{col 54}{space 4} .9241726{col 67}{space 3} .9329134
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .9295438{col 26}{space 2} .0021424{col 37}{space 1}  433.88{col 46}{space 3}0.000{col 54}{space 4} .9253448{col 67}{space 3} .9337429
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .9305317{col 26}{space 2} .0020609{col 37}{space 1}  451.51{col 46}{space 3}0.000{col 54}{space 4} .9264924{col 67}{space 3}  .934571
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .9315067{col 26}{space 2} .0019855{col 37}{space 1}  469.15{col 46}{space 3}0.000{col 54}{space 4} .9276152{col 67}{space 3} .9353983
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .9324691{col 26}{space 2} .0019163{col 37}{space 1}  486.60{col 46}{space 3}0.000{col 54}{space 4} .9287132{col 67}{space 3}  .936225
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .9334189{col 26}{space 2} .0018534{col 37}{space 1}  503.62{col 46}{space 3}0.000{col 54}{space 4} .9297862{col 67}{space 3} .9370515
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .9343562{col 26}{space 2}  .001797{col 37}{space 1}  519.96{col 46}{space 3}0.000{col 54}{space 4} .9308343{col 67}{space 3} .9378782
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .9352813{col 26}{space 2}  .001747{col 37}{space 1}  535.38{col 46}{space 3}0.000{col 54}{space 4} .9318573{col 67}{space 3} .9387053
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .9361943{col 26}{space 2} .0017035{col 37}{space 1}  549.59{col 46}{space 3}0.000{col 54}{space 4} .9328556{col 67}{space 3}  .939533
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .9370952{col 26}{space 2} .0016664{col 37}{space 1}  562.35{col 46}{space 3}0.000{col 54}{space 4} .9338291{col 67}{space 3} .9403613
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .9379842{col 26}{space 2} .0016357{col 37}{space 1}  573.44{col 46}{space 3}0.000{col 54}{space 4} .9347783{col 67}{space 3} .9411902
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .9388615{col 26}{space 2} .0016112{col 37}{space 1}  582.70{col 46}{space 3}0.000{col 54}{space 4} .9357036{col 67}{space 3} .9420195
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .9397272{col 26}{space 2} .0015928{col 37}{space 1}  589.99{col 46}{space 3}0.000{col 54}{space 4} .9366055{col 67}{space 3}  .942849
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .9405814{col 26}{space 2} .0015801{col 37}{space 1}  595.27{col 46}{space 3}0.000{col 54}{space 4} .9374845{col 67}{space 3} .9436783
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .9414243{col 26}{space 2} .0015728{col 37}{space 1}  598.55{col 46}{space 3}0.000{col 54}{space 4} .9383416{col 67}{space 3}  .944507
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .9422559{col 26}{space 2} .0015707{col 37}{space 1}  599.91{col 46}{space 3}0.000{col 54}{space 4} .9391775{col 67}{space 3} .9453344
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .9430765{col 26}{space 2} .0015732{col 37}{space 1}  599.46{col 46}{space 3}0.000{col 54}{space 4}  .939993{col 67}{space 3} .9461599
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .9438861{col 26}{space 2} .0015801{col 37}{space 1}  597.37{col 46}{space 3}0.000{col 54}{space 4} .9407892{col 67}{space 3} .9469829
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .9446848{col 26}{space 2} .0015908{col 37}{space 1}  593.85{col 46}{space 3}0.000{col 54}{space 4} .9415669{col 67}{space 3} .9478027
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .9454728{col 26}{space 2}  .001605{col 37}{space 1}  589.09{col 46}{space 3}0.000{col 54}{space 4} .9423271{col 67}{space 3} .9486185
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .9462503{col 26}{space 2} .0016222{col 37}{space 1}  583.31{col 46}{space 3}0.000{col 54}{space 4} .9430708{col 67}{space 3} .9494297
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .9470172{col 26}{space 2} .0016421{col 37}{space 1}  576.72{col 46}{space 3}0.000{col 54}{space 4} .9437988{col 67}{space 3} .9502357
{txt}{space 9}36  {c |}{col 14}{res}{space 2} .9477739{col 26}{space 2} .0016642{col 37}{space 1}  569.49{col 46}{space 3}0.000{col 54}{space 4}  .944512{col 67}{space 3} .9510357
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .9485203{col 26}{space 2} .0016883{col 37}{space 1}  561.81{col 46}{space 3}0.000{col 54}{space 4} .9452112{col 67}{space 3} .9518294
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .9492566{col 26}{space 2}  .001714{col 37}{space 1}  553.82{col 46}{space 3}0.000{col 54}{space 4} .9458972{col 67}{space 3}  .952616
{txt}{space 9}39  {c |}{col 14}{res}{space 2}  .949983{col 26}{space 2}  .001741{col 37}{space 1}  545.65{col 46}{space 3}0.000{col 54}{space 4} .9465706{col 67}{space 3} .9533953
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .9506994{col 26}{space 2}  .001769{col 37}{space 1}  537.41{col 46}{space 3}0.000{col 54}{space 4} .9472322{col 67}{space 3} .9541667
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .9514062{col 26}{space 2} .0017979{col 37}{space 1}  529.18{col 46}{space 3}0.000{col 54}{space 4} .9478824{col 67}{space 3}   .95493
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .9521033{col 26}{space 2} .0018273{col 37}{space 1}  521.05{col 46}{space 3}0.000{col 54}{space 4} .9485219{col 67}{space 3} .9556847
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .9527909{col 26}{space 2} .0018571{col 37}{space 1}  513.06{col 46}{space 3}0.000{col 54}{space 4} .9491512{col 67}{space 3} .9564307
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .9534692{col 26}{space 2} .0018871{col 37}{space 1}  505.26{col 46}{space 3}0.000{col 54}{space 4} .9497706{col 67}{space 3} .9571678
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .9541381{col 26}{space 2} .0019171{col 37}{space 1}  497.69{col 46}{space 3}0.000{col 54}{space 4} .9503806{col 67}{space 3} .9578957
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .9547979{col 26}{space 2} .0019471{col 37}{space 1}  490.36{col 46}{space 3}0.000{col 54}{space 4} .9509816{col 67}{space 3} .9586142
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .9554487{col 26}{space 2} .0019769{col 37}{space 1}  483.30{col 46}{space 3}0.000{col 54}{space 4}  .951574{col 67}{space 3} .9593234
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .9560905{col 26}{space 2} .0020064{col 37}{space 1}  476.51{col 46}{space 3}0.000{col 54}{space 4} .9521579{col 67}{space 3} .9600231
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .9567235{col 26}{space 2} .0020356{col 37}{space 1}  470.00{col 46}{space 3}0.000{col 54}{space 4} .9527338{col 67}{space 3} .9607131
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .9573477{col 26}{space 2} .0020643{col 37}{space 1}  463.77{col 46}{space 3}0.000{col 54}{space 4} .9533019{col 67}{space 3} .9613936
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .9579634{col 26}{space 2} .0020924{col 37}{space 1}  457.83{col 46}{space 3}0.000{col 54}{space 4} .9538623{col 67}{space 3} .9620645
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .9585706{col 26}{space 2}   .00212{col 37}{space 1}  452.16{col 46}{space 3}0.000{col 54}{space 4} .9544155{col 67}{space 3} .9627257
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .9591693{col 26}{space 2} .0021469{col 37}{space 1}  446.76{col 46}{space 3}0.000{col 54}{space 4} .9549614{col 67}{space 3} .9633772
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .9597598{col 26}{space 2} .0021732{col 37}{space 1}  441.63{col 46}{space 3}0.000{col 54}{space 4} .9555003{col 67}{space 3} .9640192
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .9603421{col 26}{space 2} .0021988{col 37}{space 1}  436.76{col 46}{space 3}0.000{col 54}{space 4} .9560325{col 67}{space 3} .9646516
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .9609163{col 26}{space 2} .0022236{col 37}{space 1}  432.14{col 46}{space 3}0.000{col 54}{space 4}  .956558{col 67}{space 3} .9652745
{txt}{space 9}57  {c |}{col 14}{res}{space 2} .9614825{col 26}{space 2} .0022477{col 37}{space 1}  427.76{col 46}{space 3}0.000{col 54}{space 4}  .957077{col 67}{space 3}  .965888
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .9620408{col 26}{space 2} .0022711{col 37}{space 1}  423.61{col 46}{space 3}0.000{col 54}{space 4} .9575896{col 67}{space 3}  .966492
{txt}{space 9}59  {c |}{col 14}{res}{space 2} .9625914{col 26}{space 2} .0022936{col 37}{space 1}  419.69{col 46}{space 3}0.000{col 54}{space 4}  .958096{col 67}{space 3} .9670867
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .9631343{col 26}{space 2} .0023153{col 37}{space 1}  415.99{col 46}{space 3}0.000{col 54}{space 4} .9585964{col 67}{space 3} .9676722
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .9636696{col 26}{space 2} .0023362{col 37}{space 1}  412.49{col 46}{space 3}0.000{col 54}{space 4} .9590907{col 67}{space 3} .9682485
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .9641974{col 26}{space 2} .0023563{col 37}{space 1}  409.20{col 46}{space 3}0.000{col 54}{space 4} .9595791{col 67}{space 3} .9688157
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .9647178{col 26}{space 2} .0023756{col 37}{space 1}  406.09{col 46}{space 3}0.000{col 54}{space 4} .9600617{col 67}{space 3} .9693739
{txt}{space 9}64  {c |}{col 14}{res}{space 2}  .965231{col 26}{space 2} .0023941{col 37}{space 1}  403.18{col 46}{space 3}0.000{col 54}{space 4} .9605387{col 67}{space 3} .9699232
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .9657369{col 26}{space 2} .0024117{col 37}{space 1}  400.44{col 46}{space 3}0.000{col 54}{space 4}   .96101{col 67}{space 3} .9704637
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .9662357{col 26}{space 2} .0024285{col 37}{space 1}  397.87{col 46}{space 3}0.000{col 54}{space 4} .9614759{col 67}{space 3} .9709956
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .9667276{col 26}{space 2} .0024445{col 37}{space 1}  395.46{col 46}{space 3}0.000{col 54}{space 4} .9619363{col 67}{space 3} .9715188
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .9672125{col 26}{space 2} .0024597{col 37}{space 1}  393.22{col 46}{space 3}0.000{col 54}{space 4} .9623914{col 67}{space 3} .9720335
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .9676905{col 26}{space 2} .0024742{col 37}{space 1}  391.12{col 46}{space 3}0.000{col 54}{space 4} .9628413{col 67}{space 3} .9725398
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .9681619{col 26}{space 2} .0024878{col 37}{space 1}  389.17{col 46}{space 3}0.000{col 54}{space 4} .9632859{col 67}{space 3} .9730378
{txt}{space 9}71  {c |}{col 14}{res}{space 2} .9686265{col 26}{space 2} .0025006{col 37}{space 1}  387.36{col 46}{space 3}0.000{col 54}{space 4} .9637254{col 67}{space 3} .9735276
{txt}{space 9}72  {c |}{col 14}{res}{space 2} .9690846{col 26}{space 2} .0025127{col 37}{space 1}  385.68{col 46}{space 3}0.000{col 54}{space 4} .9641599{col 67}{space 3} .9740094
{txt}{space 9}73  {c |}{col 14}{res}{space 2} .9695363{col 26}{space 2}  .002524{col 37}{space 1}  384.13{col 46}{space 3}0.000{col 54}{space 4} .9645894{col 67}{space 3} .9744831
{txt}{space 9}74  {c |}{col 14}{res}{space 2} .9699815{col 26}{space 2} .0025345{col 37}{space 1}  382.71{col 46}{space 3}0.000{col 54}{space 4}  .965014{col 67}{space 3}  .974949
{txt}{space 9}75  {c |}{col 14}{res}{space 2} .9704204{col 26}{space 2} .0025443{col 37}{space 1}  381.41{col 46}{space 3}0.000{col 54}{space 4} .9654337{col 67}{space 3} .9754072
{txt}{space 9}76  {c |}{col 14}{res}{space 2} .9708532{col 26}{space 2} .0025534{col 37}{space 1}  380.23{col 46}{space 3}0.000{col 54}{space 4} .9658487{col 67}{space 3} .9758577
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}.      marginsplot , recast(line) recastci(rline) ci1opts(fintensity(50) lpattern(dot)) xti(Risk of automation) yti("Predicted probability (95% CI)") ti("Importance of job security") saving("Figure/security.gph", replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:rti}{p_end}
{res}{txt}{p 0 4 2}
(file {bf}
Figure/security.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Figure/security.gph} saved
{com}. {c )-}
. * Combine the specified graphs into one figure
. graph combine "Figure/security.gph" "Figure/difficult.gph" "Figure/losing.gph" "Figure/dissatisfied.gph"
{res}{com}. 
. * Export the combined graph as a PDF file, replacing any existing file
. graph export "Figure/jobdisatisfactionpredictedtogetherall.pdf", as(pdf) replace
{txt}{p 0 4 2}
file {bf}
Figure/jobdisatisfactionpredictedtogetherall.pdf{rm}
saved as
PDF
format
{p_end}
{com}. 
. * Delete the individual graph files after combining them
. erase "Figure/security.gph"
. erase "Figure/difficult.gph"
. erase "Figure/losing.gph"
. erase "Figure/dissatisfied.gph"
. 
. {c )-}
{txt}
{com}. 
{txt}end of do-file

{com}. do "./Do/4_2_Figures_Appendix_ESS.do"
{txt}
{com}. *****************************************************************************
. *                                 Figures Descriptives with ESS                                 *
. *                                                                                                                                                       *                       
. * Author:                       Valentina Gonzalez Rostani                                                      *
. * Contact:                      mag384@pitt.edu                                                                 *
. * Date:                         August 9 2024                                                                                   *
. * Version:                      Stata 17                                                                                                *                                                                          
. *                                                                                                                                                       *
. *****************************************************************************
. /*
> This do-file:
> - Creates Figure A2 using data from the ESS. 
> 
> Input:
> - Data\Appendix_ESS.dta
> 
> Output:
> - Figure A2: Share routine and non-routine 2002-2018 [Figure\Share routine and non routine.pdf]
> 
> */
. 
. *Defining Directory
. cd "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication"
{res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication
{txt}
{com}. 
. *Calling the data
. use "Data\Appendix_ESS.dta", clear 
{txt}
{com}. 
. *******************************************************************************
. * Graphs
. *******************************************************************************
. * Graph style
. {c -(}
. grstyle clear
. set scheme s2color
. grstyle init
{res}{com}. grstyle set plain, box
. grstyle color background white
. grstyle color major_grid gs8
. grstyle linepattern major_grid dot
. {c )-}
{txt}
{com}. // Figure A2: Share routine and non-routine 2002-2018
. {c -(}
. preserve
. // Preserve the current dataset in memory to allow restoration later
. 
. // Generate a binary variable 'tokeep' to identify the countries to be kept for Figure A2
. gen tokeep = (cou == "BEL" | cou == "CZE" | cou == "EST" | cou == "HUN" | cou == "LUX" | cou == "SVK" | cou == "SVN" | cou == "DNK" | cou == "FIN" | cou == "FRA" | cou == "DEU" | cou == "GRC" | cou == "IRL" | cou == "NLD" | cou == "PRT" | cou == "ESP" | cou == "SWE" | cou == "GBR" | cou == "NOR" | cou == "CHE" | cou == "AUT" | cou == "ITA" | cou == "POL")
. 
. // Keep only the observations for the countries specified in 'tokeep'
. keep if tokeep == 1
{txt}(85,256 observations deleted)
{com}. 
. 
. // Keep observations where 'mnactic' is 1 or 3 (paid work or unemployed looking for a job), and 'rti' is not missing
. keep if inlist(mnactic, 1, 3) & rti ~= .
{txt}(181,328 observations deleted)
{com}. 
. // Generate a binary variable 'routine' to indicate whether the task is routine (rti > 0)
. gen routine = 1 if rti > 0
{txt}(99,690 missing values generated)
{com}. replace routine = 0 if rti < 0
{txt}(99,690 real changes made)
{com}. 
. // Calculate the total number of people in each occupation per year for each country and routine category
. bysort year routine cou: egen empl = sum(dweight)
. 
. // Calculate the total number of people in each ISCO2 category per year for each country
. bysort year cou: egen tot = sum(dweight)
. 
. // Calculate the share of people within each type of task (routine or non-routine) per year
. gen share = empl / tot * 100
. 
. // Label the variables with descriptive names
. label var empl "total no people within occup/year"
. label var tot "total no people with isco2 within year"
. label var share "share of people within type of task routinary or not /year"
. lab var cou "Country"
. 
. // Collapse the dataset to calculate the weighted share of routine and non-routine tasks by year and country
. collapse share [aw=pweight], by(routine year cou)
{res}{com}. 
. // Sort the dataset by country, year, and routine status
. sort cou year routine share
. 
. // Generate separate variables for the share of non-routine and routine tasks
. gen routine0 = share if routine == 0
{txt}(177 missing values generated)
{com}. gen routine1 = share if routine == 1
{txt}(177 missing values generated)
{com}. 
. // Calculate the total share of non-routine tasks per year and country
. bysort year cou: egen share0 = total(routine0)
. 
. // Calculate the total share of routine tasks per year and country
. bysort year cou: egen share1 = total(routine1)
. 
. // Calculate the polarization between non-routine and routine tasks
. gen polarization = share0 - share1
. 
. // Generate a line graph showing the share of routine and non-routine tasks over time for each country
. graph twoway ///
>     line share year if routine == 1, lc(red) legend(label(1 "Routine")) by(cou) || ///
>     line share year if routine == 0, lc(blue) legend(label(2 "Non-Routine")) by(cou)
{res}{com}. 
. // Export the graph to a PDF file
. graph export "Figure\Share routine and non routine.pdf", as(pdf) replace
{txt}{p 0 4 2}
file {bf}
Figure\Share routine and non routine.pdf{rm}
saved as
PDF
format
{p_end}
{com}. 
. // Restore the original dataset that was in memory before any modifications
. restore
.         
. {c )-}
{txt}
{com}. 
{txt}end of do-file

{com}. do "./Do/4_3_Figures_Appendix_CHES.do"
{txt}
{com}. *****************************************************************************
. *                                 Figure Additional Context CHES                                        *
. *                                                                                                                                                       *                       
. * Author:                       Valentina Gonzalez Rostani                                                      *
. * Contact:                      mag384@pitt.edu                                                                 *
. * Date:                         August 9 2024                                                                                   *
. * Version:                      Stata 17                                                                                                *                                                                          
. *                                                                                                                                                       *
. *****************************************************************************
. /*
> This do-file:
> - Creates Figure A4 using data from CHES. 
> 
> Input:
> - Data\1999-2019_CHES_dataset_means(v3).dta
> 
> Output:
> - Figure A4: Number of Radical Right Parties in the Party System [Figures\NewParty.pdf]
> 
> */
. 
. *Defining Directory
. cd "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication"
{res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication
{txt}
{com}. 
. *Calling the data
. use "Data\1999-2019_CHES_dataset_means(v3).dta", clear 
{txt}
{com}. 
. *******************************************************************************
. * Preparing variables
. *******************************************************************************
. {c -(}
. gen radright=1 if family==1
{txt}(1,077 missing values generated)
{com}. replace radright=0 if family>1
{txt}(1,077 real changes made)
{com}. {c )-}
{txt}
{com}. ******************************************************************************
. * Graph
. ******************************************************************************
. * Graph style 
. {c -(}
. grstyle clear
. set scheme s2color
. grstyle init
{res}{com}. grstyle set plain, box
. grstyle color background white
. grstyle set color dknavy
{res}{com}. grstyle yesno draw_major_hgrid yes
. grstyle yesno draw_major_ygrid yes
. grstyle color major_grid gs8
. grstyle linepattern major_grid dot
. grstyle color ci_area gs12%50
.  graph set window fontface "Georgia"
. {c )-}
{txt}
{com}. // Figure A4: Number of Radical Right Parties in the Party System
. {c -(}
. preserve
. collapse (sum) radright, by(year)  // Collapse the data by summing the 'radright' variable for each year
{res}{com}. 
. line radright year, ytitle("Number of Radical Right Parties", size(small))   xtitle("Year", size(small))  
{res}{com}. graph export "Figure\NewParty.pdf", as(pdf) replace
{txt}{p 0 4 2}
file {bf}
Figure\NewParty.pdf{rm}
saved as
PDF
format
{p_end}
{com}. 
. restore
. {c )-}
{txt}
{com}. 
{txt}end of do-file

{com}. do "./Do/4_4_Figures_Appendix_CMP.do"
{txt}
{com}. *****************************************************************************
. *                                 Figure Context with CMP                                               *
. *                                                                                                                                                       *                       
. * Author:                       Valentina Gonzalez Rostani                                                      *
. * Contact:                      mag384@pitt.edu                                                                 *
. * Date:                         August 9 2024                                                                                   *
. * Version:                      Stata 17                                                                                                *                                                                          
. *                                                                                                                                                       *
. *****************************************************************************
. /*
> This do-file:
> - Creates Figure A5 using data from ISSP. 
> 
> Input:
> - Data\CMP\MPDataset_MPDS2020a_stata14.dta
> 
> Output:
> -  Figure A5: Number of Nationalist Parties in Elections [Figures\Nationalist.pdf]
> 
> */
. 
. *Defining Directory
. cd "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication"
{res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication
{txt}
{com}. 
. *Calling the data
. use "Data\CMP\MPDataset_MPDS2020a_stata14.dta", clear 
{txt}(Manifesto Project Dataset Version 2020a. Please type "notes" for more details)

{com}. 
. *******************************************************************************
. * Preparing variables
. *******************************************************************************
. {c -(}
. gen year = year(edate) 
. 
. keep if countryname=="Australia" |  countryname=="Canada" |  countryname=="Greece" |  countryname=="New Zealand" |  countryname=="Portugal" |  countryname=="Spain" |  countryname=="United Kingdom" |  countryname=="United States" |  countryname=="France" |  countryname=="Norway" | countryname=="Austria" | countryname=="Belgium" | countryname=="Denmark" | countryname=="Estonia" | countryname=="Finland" | countryname=="Hungary" | countryname=="Germany" | countryname=="Iceland"  | countryname=="Ireland"  | countryname=="Italy"  | countryname=="Netherlands"  | countryname=="Norway" | countryname=="Slovakia" | countryname=="Slovenia" | countryname=="Sweden" | countryname=="Switzerland"
{txt}(1,682 observations deleted)
{com}. 
. gen radright=1 if parfam==70
{txt}(2,679 missing values generated)
{com}. replace radright=0 if parfam>70 | parfam<70
{txt}(2,679 real changes made)
{com}. {c )-}
{txt}
{com}. 
. *******************************************************************************
. * Graphs
. *******************************************************************************
. * Graph style
. {c -(}
. grstyle clear
. set scheme s2color
. grstyle init
{res}{com}. grstyle set plain, box
. grstyle color background white
. grstyle set color dknavy
{res}{com}. grstyle yesno draw_major_hgrid yes
. grstyle yesno draw_major_ygrid yes
. grstyle color major_grid gs8
. grstyle linepattern major_grid dot
. grstyle color ci_area gs12%50
.  graph set window fontface "Georgia"
. {c )-}
{txt}
{com}. // Figure A5: Number of Nationalist Parties in Elections
. {c -(}
. sort year  // Sort the dataset by year
. collapse (sum) radright, by(year)  // Collapse the data by summing the 'radright' variable for each year
{res}{com}. tsset year  // Declare the dataset to be time-series data with 'year' as the time variable
{res}
{p 0 15 2}{txt:Time variable: }{res:year}{txt:, }{res:{bind:1920}}{txt: to }{res:{bind:2019}}{txt:, but with gaps}{p_end}
{txt}{col 9}Delta: {res}1 unit
{com}. 
. rolling, window(4) saving(rolling_dataset, replace): egen ma_radright = total(radright)  // Calculate a 4-year moving average of the 'radright' variable and save the result in 'rolling_dataset.dta'
{res}{txt}(running {bf:egen} on estimation sample)
{p 0 4 2}
(file {bf}
rolling_dataset.dta{rm}
not found)
{p_end}

Rolling replications ({res}97{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
...............................................
{p 0 4 2}
file {bf}
rolling_dataset.dta{rm}
saved
{p_end}
{com}. 
. use "rolling_dataset.dta", clear  // Load the 'rolling_dataset.dta' file
{txt}(rolling: egen)
{com}. line mean start if start > 1969, title("Number of Nationalist Parties with 4-Yr Moving Average") xtitle("Year") ytitle("Number of Nationalist Parties")  // Create a line graph of the 4-year moving average of nationalist parties starting from 1970
{res}{com}. graph export "Figure\Nationalist.pdf", as(pdf) replace  // Export the graph to a PDF file and replace any existing file with the same name
{txt}{p 0 4 2}
file {bf}
Figure\Nationalist.pdf{rm}
saved as
PDF
format
{p_end}
{com}. 
. * Drop the .dta file
. erase "rolling_dataset.dta"  // Delete the 'rolling_dataset.dta' file from the directory
. 
. {c )-}
{txt}
{com}. 
{txt}end of do-file

{com}. do "./Do/4_5_Figures_Appendix_StockRobots.do"
{txt}
{com}. *****************************************************************************
. *       Figure Additional Context Stock Robot from Acemoglu & Restrepo      *
. *                                                                                                                                                       *                       
. * Author:                       Valentina Gonzalez Rostani                                                      *
. * Contact:                      mag384@pitt.edu                                                                 *
. * Date:                         August 9 2024                                                                                   *
. * Version:                      Stata 17                                                                                                *                                                                          
. *                                                                                                                                                       *
. *****************************************************************************
. /*
> This do-file:
> - Creates Figure A1 using data from Acemoglu & Restrepo. 
> 
> Input:
> - Data\reproducingacemoglu.csv
> 
> Output:
> - Figure A1: Stock of robots per thousand of workers base 1993 [Figures\number_robots.pdf]
> 
> */
. 
. *Defining Directory
. cd "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication" 
{res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication
{txt}
{com}.  
.  import delimited "Data\reproducingacemoglu.csv", clear
{res}{txt}(encoding automatically selected: ISO-8859-1)
{text}(5 vars, 22 obs)

{com}. 
.  
.  
. ******************************************************************************
. * Graph setting
. ******************************************************************************
. * Graph style 
. {c -(}
. grstyle clear
. set scheme s2color
. grstyle init
{res}{com}. grstyle set plain, box
. grstyle color background white
. grstyle set color dknavy
{res}{com}. grstyle yesno draw_major_hgrid yes
. grstyle yesno draw_major_ygrid yes
. grstyle color major_grid gs8
. grstyle linepattern major_grid dot
. grstyle color ci_area gs12%50
.  graph set window fontface "Georgia"
. {c )-}
{txt}
{com}.  //Figure A1: Stock of robots per thousand of workers base 1993
.  {c -(}
. twoway ///
>     (line germany year, lcolor(blue) lwidth(medium) lpattern(solid)) ///
>     (line denmarkfinlandfranceitalyandswed year, lcolor(gs10) lwidth(medium) lpattern(longdash)) ///
>     (line unitedstates year, lcolor(blue) lwidth(medium) lpattern(shortdash)) ///
>     (line norwayspainanduk year, lcolor(blue) lwidth(medium) lpattern(dash_dot)), ///
>     xtitle("Year") ytitle("Stock of robots per thousand of workers") ///
>     legend(order(1 "Germany" 2 "Denmark, Finland, France, Italy and Sweden" 3 "United States" 4 "Norway, Spain, and UK") position(6) size(small)) ///
>     yscale(range(0 6)) xscale(range(1993 2014)) legend(size(small))
{res}{com}. 
. 
. graph export "Figure\number_robots.pdf", as(pdf) replace
{txt}{p 0 4 2}
file {bf}
Figure\number_robots.pdf{rm}
saved as
PDF
format
{p_end}
{com}. {c )-}
{txt}
{com}. 
{txt}end of do-file

{com}. 
. 
. 
{txt}end of do-file

{com}. exit, clear
